CrowdStrike has unveiled a sweeping shift in security operations, pairing a new agentic workforce with a no-code development surface to empower security teams to design, deploy, and scale AI-driven agents. The centerpiece is a two-pronged approach: ready-to-run mission agents embedded in the Falcon platform, and Charlotte AI AgentWorks, a no-code environment that enables teams to build, orchestrate, and govern their own agents. The overarching goal is to accelerate outcomes by taking repetitive, low-value tasks off human analysts’ plates while preserving expert judgment, oversight, and defendable decision making. By infusing Falcon with agent-driven automation and equipping teams with an intuitive builder, CrowdStrike envisions a future in which every security operation can run at machine speed—without sacrificing governance or context. This strategic evolution is designed to transform routine workloads into scalable, repeatable, and auditable workflows that free analysts to concentrate on higher-value activities such as threat research, strategy, and proactive defense. The release positions CrowdStrike as a leader in translating AI-assisted operations into practical, enterprise-grade capabilities that can be adopted across diverse security environments.
The Vision behind the Agentic Security Workforce and Charlotte AI AgentWorks
The core premise of the Agentic Security Workforce is that the command center of modern security operations should be augmented by intelligent agents that operate under defender control, executing tasks at machine speed while preserving human oversight. CrowdStrike’s leadership emphasizes that the objective is not to replace analysts but to empower them with an agentic cohort that can reason, decide, and act in alignment with established security policies and risk tolerances. The goal is to minimize the time spent on repetitive triage, data normalization, and routine investigations, while ensuring that the most impactful decisions remain under the purview of skilled professionals. This vision rests on the conviction that a significant portion of incident response and threat management workflows are systematizable and scalable through carefully designed agents that encode expert knowledge and procedural best practices. By embedding the expertise of Falcon Complete analysts into reusable agent logic, the platform aims to deliver consistent outcomes across multiple engagements, reducing variance and accelerating remediation across complex environments.
Grounded in a philosophy of controllable automation, the agentic model relies on clear guardrails, auditability, and governance to maintain accountability. Analysts retain the ultimate authority to approve, adjust, or halt agent actions, ensuring a reliable checks-and-balances dynamic between human cognition and machine execution. The introduction of Charlotte AI AgentWorks further reinforces this balance by enabling teams to tailor agents to their unique environments, data schemas, and risk models without writing code. The no-code paradigm lowers the barrier to entry, democratizing AI-assisted workflows so that domain experts—not just developers—can contribute to the automation fabric. This approach invites security teams to codify playbooks, adapt to evolving threat landscapes, and continuously refine agents through real-time feedback. The combined effect is a more scalable SOC footprint that maintains high standards for accuracy, security, and governance while enabling rapid experimentation and iteration.
A distinctive feature of the vision is the emphasis on a scalable, enterprise-ready orchestration layer. The Agentic Security Platform is designed to be the backbone that coordinates multiple agents, data streams, and security tools in a unified workflow. This platform is intended to harmonize outputs from diverse sources, resolve data conflicts, and ensure consistent interpretation of signals across tools. In effect, CrowdStrike seeks to convert disparate security telemetry into a coherent, explainable, and auditable chain of actions guided by agent-driven logic. The integration with Falcon modules—each bringing its own specialized capabilities and data feeds—ensures that the agentic workforce can leverage a broad spectrum of context, from vulnerability management and threat intelligence to security information and event management. The result is a more cohesive security operation that can scale without sacrificing the depth of analysis or the precision of decision making.
The strategic narrative also foregrounds the practical benefits to security operations. By automating high-volume, repetitive tasks, the agentic workforce is designed to shrink backlogs, speed up investigations, and improve the predictability of remediation timelines. The approach aims to transform analysts’ day-to-day routines, enabling them to reallocate cognitive resources toward tasks that require expert judgment, strategic planning, and proactive threat hunting. In addition, the no-code Charlotte AI AgentWorks platform invites security teams to codify their institutional knowledge and best practices into reusable agents, which can then be deployed broadly across the organization. This model promises greater consistency in how threats are identified, prioritized, and addressed, reducing the risk of human error and enabling more precise risk management. Across the board, CrowdStrike’s vision centers on a symbiotic relationship between human expertise and intelligent automation, where agents handle repetitive, data-intensive work while analysts set strategic direction, provide oversight, and interpret nuanced signals.
From a competitive and market perspective, this strategy reflects a broader industry shift toward AI-enabled security operations that combine agent-based automation with user-friendly development environments. The emphasis on no-code agent creation resonates with organizations that need to deploy secure, scalable automation quickly without diverting scarce engineering resources into bespoke integration work. By offering mission-ready, out-of-the-box agents alongside a flexible builder for customization, CrowdStrike provides a continuum of automation options—from plug-and-play to fully tailored—without compromising central governance and security controls. The end result is a SOC that not only expands capacity but also aligns with enterprise-grade requirements for compliance, auditability, and risk management. In summary, the agentic vision positions CrowdStrike to empower security teams to operate with heightened speed, precision, and confidence, while preserving the human expertise that remains essential to credible defense.
The Dual-Pillar Architecture for Scale and Adaptability
A key element of CrowdStrike’s approach is the recognition that effective security operations demand both ready-to-use automation and customizable, environment-specific automation capabilities. Mission-ready agents deliver immediate value by addressing high-priority workflows through a curated set of AI-driven actions that reflect proven analyst decision patterns. These agents are designed to be deployed within Falcon modules and are informed by the knowledge and decisions accrued through Falcon Complete Next-Gen MDR engagements, ensuring that they reflect real-world experience and best practices. At the same time, Charlotte AI AgentWorks provides a no-code interface for security teams to design, test, and deploy their own agents. This capability enables organizations to tailor automation to their unique data models, deployment topologies, and risk tolerances, while maintaining the governance standards that enterprises require.
The dual-pillar architecture is intended to deliver both breadth and depth in automation. The ready-made agents offer rapid time-to-value and immediate uplift in automation, particularly for common and high-impact workflows that benefit from standardized logic and safeguarding. The no-code builder, by contrast, unlocks long-tail automation opportunities—supporting niche use cases, custom data formats, and organization-specific threat models. Together, these pillars are meant to empower the SOC to evolve continuously as new threats emerge and as internal processes mature. By structuring the agentic capabilities in this way, CrowdStrike seeks to reduce the friction often associated with automation adoption, such as integration complexity, policy drift, and governance gaps, while still delivering measurable outcomes across incident response, vulnerability management, threat hunting, and data reconciliation.
Importantly, the agentic framework is designed to be interoperable with existing security investments. It works with Falcon’s cloud-native architecture and its lightweight agent strategy, ensuring that deployment remains scalable and manageable across large enterprises and multi-cloud environments. The governance layer is reinforced with guardrails that guide agent behavior, enforce policy constraints, and provide audit trails for every automated decision. This level of control is crucial for regulatory compliance and for building trust in AI-enabled security operations. In essence, the dual-pillar model represents a practical blueprint for enterprises seeking to operationalize AI in a controlled, scalable, and auditable manner, while preserving the strategic role of human analysts in security decision making.
The Falcon Agentic Security Platform: Foundation of the Agentic SOC
CrowdStrike positions the Falcon Agentic Security Platform as the cornerstone of the new agentic security operations center (SOC). This platform is designed to harmonize agent-driven automation with real-time threat intelligence, unified data processing, and governance mechanisms that ensure predictable outcomes. By establishing a centralized, machine-speed execution layer, CrowdStrike aims to reduce the latency and friction that typically slow down investigations and remediation. The Falcon Agentic Security Platform is envisioned as the connective tissue that binds mission-ready agents, Charlotte AI AgentWorks-built agents, and the broader Falcon ecosystem into a cohesive operational fabric.
At the heart of the platform is the principle of defender-centric automation. All agent actions are performed in a way that preserves the defender’s oversight, enabling analysts to supervise, pause, or override automated decisions as needed. This guardrail-centric approach is intended to maintain high confidence in automated workflows while enabling rapid throughput and scalability. The platform leverages a data-rich environment where telemetry from endpoints, identities, cloud workloads, and network signals feeds agent reasoning and actions. The architecture aims to normalize data across tools, reduce data silos, and provide a consistent analytical context for agents to operate within. The end result should be more reliable reasoning, faster decision cycles, and clearer auditable traces for each automated action.
Another critical aspect of the Falcon Agentic Security Platform is its reliance on large-scale experiential learning drawn from CrowdStrike’s security operations experiences. In particular, the platform draws on millions of expert SOC decisions derived from Falcon Complete Next-Gen MDR engagements. This experiential corpus informs the agents’ reasoning, helping to align automated outputs with established best practices and organizational risk tolerances. The platform’s training data underpinning the agent logic is intended to improve accuracy, reduce false positives, and shorten investigation times by providing agents with a mature context for prioritization, correlation, and action. The implication for security teams is a more predictable automation performance, with agents that can handle routine requests at machine speed while delivering outputs that analysts can quickly validate and escalate if needed.
A notable feature of the platform is its emphasis on interoperability with Falcon modules. The first wave of agent capabilities is tightly integrated with modules such as Exposure Management, Threat Intelligence, and Next-Gen SIEM. This integration ensures that agents can access broad, security-relevant data sets, interpret vulnerability information, and participate in proactive threat hunting and detection engineering. The platform’s architecture is designed to accommodate future expansion, enabling additional agents and capabilities to be layered into the operational workflow without necessitating wholesale architecture changes. For security teams, this means a scalable path to increase automation depth as the threat surface evolves and as internal processes mature. Operational resilience is supported by the platform’s governance and security controls, which dictate how agents should interpret data, what actions they may perform, and how those actions are recorded for audit purposes.
The Falcon Agentic Security Platform is also built to optimize investigations and incident handling. By enabling agents to perform rapid triage, normalization, and preliminary analysis, investigators can focus more time on higher-order tasks such as threat hunting, strategic remediation planning, and post-incident attribution. The platform’s ability to transform raw telemetry into actionable insights at machine speed translates into tangible improvements in mean time to detect (MTTD) and mean time to respond (MTTR). In practical terms, teams can expect faster backlog reduction, more consistent triage criteria, and more reliable trigger conditions for escalation to human specialists. The platform’s design supports rapid iteration and experimentation: teams can create, test, and deploy new agent-driven workflows with governance baked in to prevent drift or policy violations. The combination of scalable automation, integrated data context, and rigorous governance positions the Falcon Agentic Security Platform as a core enabler of a modern, AI-assisted SOC.
The Seven Initial Mission-Ready Agents: Purpose, Scope, and Impact
CrowdStrike’s initial wave of mission-ready agents is designed to automate a set of high-impact, real-world workflows that historically consume significant analyst time. Each agent draws on proven expertise from Falcon Complete and enterprise-grade decision patterns to operate at machine speed. The first seven agents cover vulnerability triage, malware analysis, proactive threat hunting, query interpretation, detection-rule guidance, data standardization, and workflow automation. The design intent is to deliver concrete, measurable improvements in efficiency, threat coverage, and remediation speed while maintaining strict governance and traceability for every action.
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Exposure Prioritization Agent (Falcon Exposure Management): This agent automates vulnerability triage by prioritizing exploitable risks and directing remediation efforts toward those with the greatest potential impact. By analyzing exposure data, asset criticality, and exploit likelihood, the agent generates a prioritized remediation backlog, enabling security teams to allocate scarce resources to the most urgent risks. The expected benefits include backlog reduction, faster remediation cycles, and improved focus on high-severity threats. The agent is designed to integrate with existing exposure management workflows and to adapt to evolving vulnerability data streams, ensuring that prioritization remains aligned with current threat landscapes and organizational risk appetites.
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Malware Analysis Agent (Falcon Threat Intelligence): Tasked with analyzing files, mapping malware families, and generating YARA rules, this agent shifts defense from file-by-file analysis to family-level reasoning. By clustering characteristics and mapping behavior to family archetypes, the agent enhances the efficiency of malware classification and rule generation. This enables defense at a higher abstraction level, reducing repetitive manual analysis and expediting protective measures. The agent is designed to ingest intelligence feeds, apply heuristic and static/dynamic analysis results, and produce actionable artifacts that can be integrated into detection pipelines and containment strategies.
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Hunt Agent (Falcon Threat Intelligence): Focused on proactive threat hunting, this agent automates continuous scanning for emerging threats. It leverages threat intelligence, behavioral indicators, and IOC/IOA patterns to seed hunting campaigns, streamlining the discovery of novel adversary techniques. By maintaining a persistent hunt loop, the agent accelerates the discovery of new attack vectors and surfaces potential compromises earlier in the kill chain. The agent’s output supports investigators with prioritized leads, context-rich alerts, and rapid triage guidance, allowing analysts to pursue high-value hunting activities with higher confidence and efficiency.
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Search Analysis Agent (Falcon Next-Gen SIEM): This agent summarizes and interprets complex query results within seconds, replacing hours of manual analysis. By distilling large data sets into concise, actionable insights, it enhances the speed and quality of investigations. The agent is designed to work within the SIEM environment to provide clear narratives, highlight critical correlations, and surface context that informs decision making. The automation reduces cognitive load on analysts and improves the consistency of interpretation across investigations, contributing to more reliable outcomes.
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Correlation Rule Generation Agent (Falcon Next-Gen SIEM): Focused on advanced threat detection and insider risk, this agent recommends and tunes detection rules based on observed behaviors and emerging patterns. It translates raw telemetry into finely tuned rules that improve detection coverage while minimizing noise. The agent supports investigators by providing rationale for suggested rule changes and by validating the impact of new rules on existing detections. This capability helps maintain an adaptive, risk-aware defense posture in the face of evolving adversaries and changing enterprise environments.
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Data Transformation Agent (Falcon Next-Gen SIEM): This agent normalizes and translates data across tools, eliminating data inconsistencies that can impede automation. By reconciling disparate formats and schemas, it reduces errors that stall automation pipelines and ensures that downstream workflows receive clean, interoperable inputs. The normalization process supports more reliable analytics, faster automation, and better cross-tool collaboration. In practice, teams gain smoother data flows, which translate into more accurate detections, faster investigations, and greater trust in the automated decisions.
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Workflow Generation Agent (Falcon Next-Gen SIEM): Perhaps the most user-facing of the seven, this agent converts natural language into automated workflows within Falcon Fusion, with no coding required. Analysts can describe desired outcomes, data sources, and orchestration steps, and the agent translates that intent into executable automation. This capability dramatically accelerates the creation and deployment of new workflows, enabling rapid experimentation and iteration. It also lowers the barrier to automation, allowing teams to codify and share successful playbooks, and to scale them organization-wide with governance and version control.
Together, these seven mission-ready agents illustrate a concrete strategy: automate high-volume, repeatable security tasks while enabling analysts to devote their time to interpretation, strategy, and complex investigations. Each agent is designed to operate within defined guardrails, with clear inputs and outputs, and with traceable actions that support post-hoc review. The practical implication is a more efficient SOC that can process larger workloads, respond faster to evolving threats, and maintain a high level of confidence in automated outcomes. In addition, by aligning agent capabilities with Falcon modules and existing workflows, these agents can be adopted incrementally, enabling organizations to realize measurable improvements without overhauling current security ecosystems. The mission-ready approach thus offers a pragmatic path to scalable automation, enabling teams to scale expertise while maintaining control and accountability.
Charlotte AI AgentWorks: Build and Customize Agents
In parallel with the ready-made agents, CrowdStrike’s no-code Charlotte AI AgentWorks platform provides security teams with a powerful environment to craft, test, and deploy their own agents. This capability transforms every security team into an AI builder and orchestrator, capable of tailoring automated behavior to the specifics of their environment. The core promise is simplicity without sacrificing governance: teams can describe the mission, specify the data inputs, and define the agent’s behavior using natural language, without writing code. The resulting agents can be built, tested, and deployed directly within the Falcon platform, enjoying enterprise-grade security, governance, and controls that align with organizational policies and regulatory requirements.
The no-code paradigm is designed to lower the friction associated with automation adoption. Analysts, threat researchers, and security engineers can collaborate to translate their playbooks into automated agents, iterating quickly based on feedback and changing threat landscapes. The platform emphasizes safety and accountability, ensuring that agent actions are auditable, reversible when necessary, and aligned with risk thresholds. By enabling rapid experimentation and deployment, Charlotte AI AgentWorks helps organizations scale their automation efforts more effectively, expand coverage to less-memory-resourced workflows, and codify institutional knowledge into reusable, governed agents. The builder is designed to maintain strong data governance and access controls, ensuring that agents operate only on permitted data sets and within defined security boundaries. This is essential for maintaining trust in AI-driven operations and for meeting compliance requirements across industries. The combined effect is a powerful synergy: mission-ready agents provide immediate value for common tasks, while Charlotte AI AgentWorks empowers teams to craft bespoke automation that precisely matches their operational realities and strategic aims.
Analysts can guide and collaborate with agents in real time, creating a collaborative defense model that merges human insight with machine efficiency. The platform supports secure connections and cross-ecosystem collaboration, enabling agents to interact with trusted third-party agents and tools in support of broader enterprise workflows. In practical terms, this means teams can orchestrate multi-agent workflows across the security stack, integrating data, detections, and response actions in a single, coherent environment. The emphasis on governance, safety, and accountability ensures that the collaboration remains auditable and compliant, while still delivering the speed and scale required to handle modern attack surfaces. In sum, Charlotte AI AgentWorks extends the agentic paradigm from ready-made capabilities to customizable, environment-specific automation, enabling organizations to tailor the agentic security posture to their unique risk profiles and operational priorities.
The Agentic Workforce: Judgment, Oversight, and Real-Time Collaboration
A central pillar of the agentic model is the integration of elite analyst judgment into automated workflows. The Agentic Workforce is trained on millions of expert SOC decisions associated with Falcon Complete Next-Gen MDR, equipping agents with a framework for reasoning, oversight, and guardrails. Analysts can guide and supervise agents in real time, shaping behavior and ensuring that automated actions are aligned with current safety and governance standards. This collaborative dynamic centralizes agentic defense within a single, scalable platform, enabling teams to leverage the collective expertise of the SOC while extending it through automation. The result is a defense posture that can scale expertise, accelerate investigations, and produce more consistent outcomes across incidents and routine operations alike.
The collaboration model also recognizes the value of multi-agent coordination within the broader enterprise ecosystem. Charlotte AI can securely connect with trusted third-party agents, enabling a broader operational horizon. By providing a trusted command plane for human–machine and multi-agent collaboration, the platform enables enterprises to extend agentic capabilities across diverse tools and environments without compromising security or governance. The design emphasizes secure data handling, strict access controls, and traceability of all interactions among agents and humans. This approach helps ensure that complex workflows—such as cross-tool correlation, adaptive detection, and automated containment—can be executed with confidence and auditable transparency. By uniting machine-driven automation with human expertise and external agent capabilities, CrowdStrike envisions a more resilient, responsive, and scalable security operations framework.
All customers on the Falcon platform are set to gain access to Charlotte AI and the first wave of mission-ready agents, subject to a practical credit model that supports ongoing use. The credits concept is designed to balance automation demand with governance and cost control, ensuring that organizations can incrementally adopt and expand agent-driven automation in a measured, predictable manner. The combination of mission-ready agents, Charlotte AI AgentWorks, and this governance framework is positioned to help security teams move from reactive response to proactive defense, enabling them to anticipate risk, automate common tasks, and scale expertise across large and distributed environments. The broader message is one of empowerment: organizations can rapidly enhance their security posture by leveraging AI-driven agents, while retaining the human oversight and strategic control that defines effective risk management and enterprise security excellence.
The Falcon Agentic Security Platform: Foundation of the Agentic SOC
CrowdStrike presents the Falcon Agentic Security Platform as the structural bedrock for a new era of agent-driven security operations. This platform is conceived as the central nervous system of the agentic SOC, coordinating intelligent agents, data flows, and governance to deliver rapid, scalable, and auditable outcomes. The platform’s design emphasizes defender control, ensuring that automation acts as a force multiplier rather than a substitute for human judgment. In practice, this means agents operate under clearly defined policies, with built-in safeguards that prevent unintended actions, and with robust logging that enables post-incident analysis and compliance reporting. The governance framework is integral to the platform, providing the controls necessary to manage risk while still enabling the speed and agility that automation offers.
A foundational objective of the platform is to harmonize telemetry from across the CrowdStrike Falcon ecosystem. This includes signals from endpoints, identity services, cloud workloads, and network considerations. By unifying data streams, the Falcon Agentic Security Platform enables agents to reason with a richer, more contextual understanding of the enterprise environment. The result is more accurate triage, more relevant remediation suggestions, and more precise threat hunts, all while maintaining a clear chain of custody for decisions and actions. The platform’s architecture supports interoperability with existing security investments, ensuring that organizations can adopt agentic capabilities without abandoning tools they rely on. This interoperability is essential for enterprises with complex toolchains, multi-cloud footprints, and diverse regulatory requirements.
A distinctive aspect of the platform is its reliance on the wealth of experiential knowledge amassed through Falcon Complete Next-Gen MDR services. Millions of expert SOC decisions inform agent behavior, enabling agents to operate with a level of professional judgment that mirrors human experts. This experiential base helps agents interpret context, prioritize actions, and apply best-practice guardrails. By institutionalizing this knowledge within the platform, CrowdStrike aims to reduce the variability often observed in automated responses and to provide a consistency benchmark across different teams and environments. The platform’s decision-making logic is designed to be transparent, enabling analysts to trace how an action was derived, what data influenced the decision, and how it fits within the larger incident response workflow. This traceability is critical for auditability, risk assessment, and continuous improvement of automated processes.
From a technical perspective, the Falcon Agentic Security Platform is designed to support scalable deployment across large organizations. It leverages a cloud-native architecture that minimizes on-premises complexity, simplifies provisioning, and accelerates time-to-value. The lightweight agent paradigm remains central, reducing performance overhead on endpoints while enabling broad visibility and control across devices, services, and environments. The platform is engineered to handle the velocity and volume of modern security telemetry, applying intelligent filtering, normalization, and orchestration to ensure that the right actions are taken at the right time. In addition, it provides governance capabilities that capture how decisions are made, who authorized actions, and how those actions were executed, which is essential for security audits, regulatory compliance, and post-incident learning.
Operationally, the Falcon Agentic Security Platform is designed to accelerate investigations and post-incident analysis. By enabling agents to perform rapid triage, data normalization, and preliminary analysis, investigators can focus more on complex attribution, strategic remediation planning, and proactive threat hunting. The platform’s orchestration capabilities help teams coordinate multiple agents, data sources, and response actions into cohesive workflows. This orchestration is particularly valuable in large, distributed environments where sequential or parallel tasks must be aligned and managed under a single command structure. The end state is a more efficient, expressive, and reliable security operations workflow that scales with the organization’s threat surface and evolving business needs.
Interoperability and Data Integrity
A central promise of the Falcon platform is interoperability with existing security ecosystems. The agentic architecture is designed to work with Falcon’s modular, cloud-native approach, enabling organizations to augment or extend their current defenses without disruptive migrations. Data integrity and consistency are prioritized through standardized data transformation and normalization processes, which facilitate seamless sharing of context across agents and tools. This standardization helps reduce operational friction and enhances the accuracy of automated decisions. The platform’s commitment to data quality underpins the trust analysts place in automated outputs, a critical factor for adoption at scale in enterprise settings.
Security governance is another fundamental pillar. The platform enforces strict access controls, role-based permissions, and policy enforcement to ensure that agents operate within approved boundaries. All agent actions are traceable, auditable, and reversible where appropriate, enabling organizations to meet regulatory requirements and conduct thorough investigations when needed. This governance framework also supports compliance reporting and audit readiness, helping to demonstrate due diligence in automated operations. The emphasis on guardrails, accountability, and traceability aims to reassure security leadership that automation augments, rather than undermines, organizational risk management.
The Falcon Agentic Security Platform is also designed to accommodate growth and evolution. As new data sources emerge, or as new threat surfaces require more sophisticated analysis, the platform can incorporate additional agents and capabilities without destabilizing core operations. This extensibility is essential for maintaining relevance in a rapidly changing threat landscape. By providing a scalable platform, CrowdStrike aims to empower organizations to expand automation coverage across more security domains, adapt to regulatory changes, and continuously refine their security posture based on real-world experiences and feedback from security operations teams.
In summary, the Falcon Agentic Security Platform is positioned as the backbone of a modern, AI-enabled SOC. It is designed to deliver speed, scalability, and governance in a cohesive, interoperable package that complements and enhances existing security investments. By combining data-rich telemetry, experiential knowledge, and robust guardrails within a cloud-native architecture, the platform aspires to turn automation into a trusted, scalable capability that broadens the reach and impact of security operations across the enterprise.
Mission-Ready Agents: Automating Analyst Workflows
The introduction of mission-ready agents marks a significant shift in how organizations think about security workflow automation. These out-of-the-box agents are designed to handle critical, high-impact workflows at machine speed, thereby freeing human analysts to concentrate on strategic analysis, threat hunting, and complex investigations. The agents are built on the premise that many security tasks are repetitive yet essential, and that automating these tasks can dramatically improve throughput, consistency, and outcomes. By leveraging the depth of expertise embedded from Falcon Complete, the agents operate with a level of reasoning, decision making, and action that mirrors elite human analysts while maintaining strict guardrails to ensure controlled execution. This combination of speed and competence is intended to produce measurable improvements in detection, triage, and containment times, while preserving the accountability and oversight that security teams require.
The initial suite of mission-ready agents is designed to address seven core workflows, each representing a high-value area where automation can provide meaningful impact. These agents are integrated with Falcon modules and are informed by the platform’s extensive data and analytical capabilities. The breadth of their capabilities is intended to cover common attack surfaces and operational pain points, delivering a practical, scalable automation layer that can be deployed incrementally across an enterprise. Each agent has a clearly defined remit, inputs, and outputs, along with governance and traceability that allow analysts to validate results, adjust thresholds, and escalate when necessary. The goal is to create a reliable, repeatable automation fabric that reduces cognitive load on analysts while preserving the quality and integrity of security decisions.
The operational premise behind mission-ready agents is that automation should deliver fast, reliable outcomes without compromising safety or policy compliance. Agents work within predefined guardrails, guided by enterprise risk policies and security best practices. They can interact with a range of Falcon components and external data streams, integrating context to produce actionable insights. By processing large volumes of data and applying consistent decision logic, the agents can identify prioritization signals, surface critical indicators, and execute standardized remediation steps where appropriate. This accelerates the early stages of incident response, vulnerability management, and threat detection, enabling analysts to spend more time on higher-order tasks such as strategic threat assessment and coordination with other security functions.
A Closer Look at Each Mission-Ready Agent
Exposure Prioritization Agent (Falcon Exposure Management): The purpose of this agent is to automate vulnerability triage by focusing remediation efforts on exploitable risks. It gathers exposure data, assesses risk based on asset importance and exposure severity, and then generates a prioritized backlog for remediation. The aim is to shrink backlogs and ensure that security teams allocate resources toward the most impactful vulnerabilities. This approach helps organizations improve risk posture by expediting remediation strategies for the most critical exposure points, reducing the likelihood of exploit chains forming from overlooked weaknesses.
Malware Analysis Agent (Falcon Threat Intelligence): This agent handles file-level analysis and maps malware families, guiding defense at a higher abstraction level than analyzing individual files. By generating YARA rules and placing files into family-based contexts, it enables faster, more scalable defense. The agent’s workflow emphasizes family-level containment and strategy, which can lead to more efficient threat modeling and rapid deployment of family-specific indicators of compromise. The resulting structure supports more consistent threat containment and faster responses to new malware developments.
Hunt Agent (Falcon Threat Intelligence): This agent automates proactive threat hunting by continuously monitoring for emerging threats. It leverages threat intelligence signals and behavioral indicators to seed and drive hunting campaigns, generating leads that analysts can pursue with higher confidence. The continuous-scanning approach helps detect previously unseen attacker techniques, improving early warning and reducing dwell time. The agent’s capability to orchestrate regular hunting activities across the threat landscape aims to maintain persistent vigilance and strengthen proactive defense.
Search Analysis Agent (Falcon Next-Gen SIEM): This agent specializes in summarizing and interpreting query results quickly, transforming hours of manual analysis into succinct, digestible insights. By providing clear narratives and context, it accelerates investigations and reduces cognitive load on analysts. The agent can be integrated into SIEM workflows to streamline information retrieval, correlate signals, and present prioritized findings that guide subsequent steps. This capability is especially valuable in complex investigations where large data volumes can obscure critical insights.
Correlation Rule Generation Agent (Falcon Next-Gen SIEM): This agent focuses on improving detection efficacy by recommending and tuning detection rules for advanced threats and insider risks. It translates observed patterns into refined rules, helping to maintain robust monitoring while reducing noise and false positives. The agent supports analysts by explaining the rationale behind proposed rule changes and validating their impact on the detection landscape. The goal is to sustain a dynamic, adaptive detection regime that stays aligned with evolving threat behaviors and organizational risk tolerances.
Data Transformation Agent (Falcon Next-Gen SIEM): This agent standardizes and translates data across multiple tools, removing data-quality bottlenecks that hinder automation. By normalizing divergent data formats and schemas, it ensures that downstream automation pipelines can operate with consistent, error-free inputs. The benefits include more reliable automation, fewer data translation errors, and smoother end-to-end workflows. The agent’s capability to unify data streams is foundational for scalable, cross-tool orchestration and joint analysis.
Workflow Generation Agent (Falcon Next-Gen SIEM): The Workflow Generation Agent converts natural language into automated workflows within Falcon Fusion, eliminating the need for coding. Analysts describe the desired objective, data sources, and orchestration steps, and the agent translates that intent into executable automation. This dramatically accelerates the creation and deployment of new workflows, enabling rapid experimentation and iteration while ensuring consistent governance. The agent supports versioning, testing, and rollback controls, ensuring that new workflows can be deployed with confidence and oversight.
The seven mission-ready agents together create a robust automation spine that addresses core, high-impact security activities. Each agent is designed to operate within clearly defined boundaries, with well-specified inputs, outputs, and decision criteria. They are built to work in concert with Falcon modules, providing a coherent and scalable automation layer that enhances threat visibility, accelerates investigations, and shortens remediation windows. The practical outcomes organizations can anticipate include faster triage, improved risk prioritization, streamlined malware analysis, and more effective threat hunting. Moreover, because these agents are designed to be governed and auditable, security teams can demonstrate compliance and governance while still reaping the efficiency gains of automation. This combination of capability and discipline is intended to deliver measurable improvements in SOC throughput, accuracy, and confidence in automated actions.
Governance and Safeguards in Automated Workflows
In parallel with the expansion of automation capabilities, CrowdStrike emphasizes governance as an integral, non-negotiable aspect of the agentic model. All mission-ready agents operate under supervisory controls that can be configured by security leaders to reflect organizational policies, regulatory requirements, and risk tolerances. This governance posture ensures that automation remains aligned with business objectives and security standards, while still delivering the speed and scalability needed to respond to evolving threats. Guardrails are designed to prevent unintended actions, enforce data access policies, and ensure auditable trails for every automated decision and action. The emphasis on transparency, traceability, and accountability is essential for enterprise adoption, enabling auditors and security leadership to review agent outputs, understand the basis for decisions, and assess risk exposure.
The design also prioritizes risk management in automation. Agents are built to minimize the likelihood of false positives and to escalate cases when human review is necessary. The governance framework supports exceptions for high-risk scenarios, enabling analysts to pause or override automated actions when warranted. By ensuring that automated processes remain auditable and controllable, the platform helps organizations balance the benefits of automation with the need for careful risk management. In practice, this means that the agentic workflow can scale across thousands of endpoints and data sources while preserving the ability to audit, review, and refine automation logic over time. The governance layer is the anchor that helps organizations navigate the tension between speed and safety in AI-enabled security operations.
Charlotte AI AgentWorks: Build and Customize Agents
Charlotte AI AgentWorks transforms security teams into builders of their own AI-powered agents without writing code. This no-code environment allows analysts and engineers to specify the mission, select data inputs, and define how agents should behave using natural language prompts. The result is a set of agents that can be deployed directly within the Falcon platform, with enterprise-grade security, governance, and compliance controls baked in. The no-code approach reduces barriers to automation and accelerates the translation of domain expertise into automated workflows that can scale across the organization.
The platform emphasizes practical ease of use alongside robust governance. Users can unleash creative automation while maintaining strict access controls, versioning, and auditability. Agents built with Charlotte AI AgentWorks can be tested in isolated environments before being rolled into production, ensuring that new automation behaves as intended and complies with organizational risk policies. The ability to test, validate, and deploy agents quickly supports rapid experimentation, enabling teams to refine automation strategies in response to changing threat landscapes and internal process improvements. This capability is particularly valuable for security programs that require agility to address new attack techniques or operational requirements without sacrificing control and reliability.
In practice, Charlotte AI AgentWorks enables a collaborative workflow where multiple stakeholders—threat researchers, incident responders, IT operations, and security governance teams—can contribute to the automation fabric. The platform encourages cross-functional co-creation by making it possible to codify best practices, playbooks, and institutional knowledge into reusable agents. The resulting automation artifacts can be shared, versioned, and governed, enabling organizations to scale their automation investments with confidence. The no-code approach also democratizes automation, allowing subject matter experts who deeply understand specific business contexts to contribute directly to automated defense strategies. This collaborative, inclusive model can help security programs capture tacit knowledge and convert it into explicit, repeatable workflows that improve consistency and efficiency.
From a governance perspective, no-code agent development is designed to operate within enterprise-scale security policies. Access controls, data handling rules, and compliance requirements are integrated into the builder, ensuring that new agents align with regulatory obligations and organizational risk standards. Auditing and reporting capabilities provide visibility into who created an agent, what data it used, what actions it took, and what outcomes were achieved. This transparency supports risk assessment, incident post-mortems, and continuous improvement by making automation outcomes visible to stakeholders and auditors alike. The result is a scalable, governed automation program that can rapidly respond to evolving threats while maintaining accountability and oversight.
The practical implications for security teams include faster automation development, improved consistency across workflows, and the ability to tailor automation to the specifics of an organization’s data and risk posture. Teams can build agents that reflect their unique threat profiles, compliance requirements, and operational constraints, then deploy them with confidence within the Falcon ecosystem. The combination of no-code agent creation and enterprise-grade governance enables organizations to move beyond one-size-fits-all automation toward a personalized, scalable automation strategy. The impact on security operations can be substantial: faster deployment of new playbooks, more precise handling of routine tasks, and better alignment between automated actions and organizational priorities.
Real-Time Collaboration and Multi-Agent Orchestration
Charlotte AI AgentWorks is designed to support real-time collaboration between agents and with human analysts. The platform provides a secure channel for coordinating multi-agent workflows, enabling agents to communicate, share context, and collaborate on complex tasks. With the advent of third-party agent integrations, the orchestration surface expands beyond a single vendor’s ecosystem to create a more holistic defense network. This multi-agent coordination can enable more comprehensive coverage of threat landscapes, improved cross-tool correlation, and more efficient response pipelines. The governance framework remains in place to ensure that cross-agent interactions adhere to security policies and compliance requirements, with auditable traces for all inter-agent actions and human interventions.
The broader enterprise implications of this collaboration model include improved incident response coordination, broader automation coverage, and enhanced resilience through partner-enabled capabilities. By enabling Charlotte AI to act as a centralized command plane, organizations can coordinate human-machine and multi-agent workflows from a single interface. This reduces fragmentation, increases visibility, and helps ensure that responses are aligned with the organization’s strategic risk posture. The collaboration design supports scalable defense across a diverse set of security tools and services, enabling teams to leverage the strengths of different agents and partners while maintaining a consistent governance framework. In practice, this means more coordinated, efficient, and auditable responses to security events, with automation serving as a force multiplier rather than a substitute for human expertise.
Access Model and Enterprise Adoption
All CrowdStrike platform customers gain access to Charlotte AI including the initial set of mission-ready agents, subject to a practical credits model designed to manage usage and governance. The credits construct provides a structured approach to automation adoption, helping organizations balance demand, cost, and governance considerations as they scale automation across the enterprise. This approach supports gradual rollout, enabling teams to begin with core workflows and progressively extend automation coverage as confidence and governance controls mature. By integrating Charlotte AI and mission-ready agents into the Falcon platform, CrowdStrike offers a cohesive, scalable automation capability that aligns with enterprise procurement and security governance processes.
From an adoption perspective, organizations can expect a smoother transition to AI-assisted operations, thanks to no-code agent development, out-of-the-box workflows, and a governance-first design. This combination reduces the friction typically associated with automation, such as lengthy development cycles, integration challenges, and policy gaps. The end result is a practical, scalable path to advancing security operations through AI-powered agents that complement and amplify human expertise. The inclusion of third-party collaboration and multi-agent orchestration further enhances the potential for broader, enterprise-wide improvements in detection, response, and threat management.
The Analyst Judgment, Real-Time Collaboration, and Enterprise Integration
The agentic model hinges on preserving expert judgment while leveraging AI-driven automation to accelerate outcomes. Trained on millions of real-world SOC decisions, the agent workforce is designed to operate with reasoning, oversight, and guardrails that keep automation aligned with human expertise. Analysts can guide and collaborate with agents in real time, effectively merging human insight with machine speed within a unified platform. This design aims to centralize agentic defense in a single, scalable environment, enabling organizations to scale expertise, speed up investigations, and improve outcomes across security operations.
The collaboration framework extends beyond internal teams to include trusted third-party agents and partners. Charlotte AI’s ability to securely connect with partner agents expands the reach of agentic defense into the broader enterprise ecosystem. This multi-agent collaboration model offers opportunities to leverage specialized capabilities from external providers, enriching the defense through diverse data sources, detection methods, and response actions. While expanding capabilities, CrowdStrike emphasizes maintaining rigorous security, governance, and data protection standards to ensure that cross-organizational collaboration remains safe, auditable, and under supervisory control.
For organizations considering broader adoption, the agentic model provides an integrated approach to balancing speed and precision. By combining machine-speed automation with human oversight, teams can pursue faster investigations, more consistent remediation, and higher confidence in automated decisions. The central command plane—Charlotte AI—facilitates coordination across multiple agents and tools, enabling a comprehensive defense approach that transcends siloed toolsets. This orchestration is particularly valuable for large enterprises with distributed security operations centers, complex data ecosystems, and varying regulatory obligations, as it supports a cohesive, scalable, and auditable automation program.
Governance, compliance, and risk management are central to this model. The agentic workforce relies on guardrails, policy enforcement, and auditable actions to ensure ethical, legal, and risk-conscious automation. Analysts retain decision authority and can override automated actions when necessary, providing a human-in-the-loop safety mechanism that strengthens overall security posture. The combination of automated efficiency and human discernment is designed to deliver faster incident response, improved threat detection, and more effective risk management, all while preserving accountability and traceability across the automation lifecycle.
Implementation and Operational Considerations
For organizations pursuing this transformation, practical considerations include data governance, access management, and integration alignment with existing security investments. Institutions should assess data flows, data quality, and the potential impact of automation on compliance requirements. A phased deployment strategy—starting with high-value, low-risk workflows and gradually expanding to more complex scenarios—can help organizations build confidence in the agentic model while maintaining tight governance controls. Training and enablement are also critical, ensuring security teams understand how to configure guardrails, interpret agent outputs, and intervene when needed. By combining well-defined deployment plans with robust governance, organizations can maximize the effectiveness of the agentic platform while mitigating risk.
Operational metrics will be essential to measuring success. Potential indicators include reductions in mean time to triage and mean time to remediation, improvements in backlog management, increased detection coverage with lower false-positive rates, and enhanced analyst productivity. Organizations may also track governance-related metrics, such as the frequency of overrides, auditability scores, and compliance alignment. By establishing clear KPIs and feedback loops, teams can continuously refine agent behavior, optimize workflows, and ensure that automation remains aligned with strategic security objectives and regulatory requirements. The agentic model is designed to be iterative, with ongoing refinements that adapt to changing threats, evolving business needs, and lessons learned from incidents and investigations.
In essence, the agentic strategy represents a holistic reimagining of security operations: automation at machine speed, guided by human judgment, built on a no-code platform that democratizes AI-enabled defense, and governed by enterprise-grade controls that ensure safety, accountability, and compliance. The outcome is a more scalable, resilient, and proactive security posture that can adapt to the relentless pace of modern cyber threats while preserving the strategic value of human expertise. As organizations begin to adopt and scale these capabilities, the security function stands to become more efficient, more accurate, and more capable of anticipating and mitigating risk in an increasingly complex digital landscape.
Access, Governance, and Enterprise Adoption
Access to the Charlotte AI platform and the mission-ready agent suite is structured to support enterprise-scale deployment while maintaining robust governance. All Falcon platform customers are positioned to gain entry to Charlotte AI, including the core set of mission-ready agents, under a model designed to manage usage through credits. This approach seeks to balance the need for rapid adoption with the necessity of governance controls and cost awareness. By providing a measured framework for enabling automation at scale, CrowdStrike aims to help security teams grow their automation footprint in a controlled and predictable manner. The credits mechanism is intended to provide visibility into automation consumption, enabling organizations to forecast resource needs, budget effectively, and prevent unintended overuse.
From an enterprise perspective, the governance framework is a central pillar of the agentic strategy. Security teams can set and enforce policy constraints that guide how agents operate, what data they may access, and what actions they may perform. The governance model also supports auditable logging and traceability for all agent-driven decisions and actions, which is critical for compliance reporting, internal audits, and post-incident reviews. In large organizations, this level of visibility helps governance teams validate that automation is functioning as intended, remains aligned with risk tolerance, and can be fully explained to regulators or auditors if required. The combination of a structured access model and comprehensive governance is designed to reduce risk while enabling the broad adoption of AI-powered automation across the enterprise.
The enterprise can also expect that Charlotte AI AgentWorks will continue to evolve, offering enhanced capabilities for building and deploying custom agents. As organizations deepen their automation strategies, they may wish to broaden the range of workflows automated through Charlotte AI and extend agent collaboration across more business units and technical domains. The platform’s no-code paradigm supports rapid prototyping and iterative improvements, enabling teams to respond quickly to changing threat landscapes, regulatory requirements, and business priorities. With governance and data protection built into the builder, organizations can maintain control over who creates what, what data is used, and how agents interact with sensitive information. This modeling supports scalable automation adoption while ensuring that security standards and risk constraints remain intact.
Organizations adopting the agentic model should also consider the broader implications for security program strategy and workforce development. As automation handles more routine and data-driven tasks, analysts can pivot toward higher-impact activities, such as advanced threat detection, strategic threat modeling, and cross-functional security governance. This shift may also necessitate new skill sets and training to maximize the value of AI-driven workflows and to manage the evolving role of AI-assisted defense within the SOC. The agentic framework is designed to complement existing capabilities, not replace them, by expanding the capabilities of security teams and enabling more effective collaboration between human and machine agents. The result is a more agile, scalable, and resilient security program that can respond to threats with speed and precision while maintaining the oversight and accountability demanded by enterprise environments.
Conclusion
CrowdStrike’s launch of the Agentic Security Workforce represents a comprehensive evolution of security operations, combining mission-ready automation with a no-code agent development platform to empower security teams to design, deploy, and govern AI-driven agents. The dual-pillar approach—out-of-the-box mission-ready agents and Charlotte AI AgentWorks—creates a scalable continuum that supports both quick wins and long-term customization. By embedding the expertise of Falcon Complete and integrating with the Falcon platform, CrowdStrike aims to deliver machine-speed capabilities that accelerate remediation, enhance threat detection, and free analysts to focus on higher-value work. The Falcon Agentic Security Platform serves as a robust foundation, offering governance, data integrity, and interoperability with existing security investments, while enabling rapid deployment and continuous improvement of automated workflows.
The seven initial mission-ready agents demonstrate a concrete commitment to automating critical workflows spanning vulnerability prioritization, malware analysis, proactive hunting, SIEM-driven analysis, rule generation, data normalization, and workflow generation. Together with Charlotte AI AgentWorks, organizations gain a powerful no-code environment to tailor automation to their unique environments, expand automation coverage, and codify institutional knowledge into reusable, governed agents. The platform’s emphasis on collaboration—across internal teams and trusted third-party agents—expands the reach of automation and enhances the enterprise’s ability to coordinate defense across a broad ecosystem, all within a secure command plane that maintains control and auditability.
For enterprises evaluating AI-enabled security, the agentic approach offers a practical path to scale expertise, reduce manual toil, and improve incident response outcomes without compromising governance or regulatory compliance. As organizations adopt these capabilities, they should approach implementation with a structured plan: start with high-impact, low-risk workflows, establish clear governance and auditing, train staff on new processes, and monitor outcomes with defined metrics. With the Agentic Security Workforce and Charlotte AI AgentWorks, CrowdStrike presents a comprehensive framework that blends human judgment with AI-driven automation to create a more proactive, scalable, and resilient security operation—one that is better equipped to defend the enterprise in an era of rapidly evolving threats.