Key takeaways
Autonomous operation: Agentic AI systems act with programmed autonomy over time, monitoring progress and adapting strategies while maintaining strict authentication and authorization controls throughout their lifecycle.
Security implications: AI-powered agents operating on behalf of users, systems, or organizations create new identity-related risks that require governance approaches beyond static access controls or traditional human-centric models.
Enterprise requirements: Moving beyond passive content generation to intelligent action requires dynamic, policy-based identity governance to operate safely at scale.
Strategic imperative: IT and security teams must stay current with the rapid evolution of agentic AI capabilities and continuously adapt their identity management strategies to match technological advancements.
How does agentic AI work?
Agentic AI systems operate through four core capabilities, each requiring robust identity management to function securely in enterprise environments:
Autonomy with identity controls: Acts independently within predefined security boundaries, accessing systems through verified credentials and authenticated API calls while maintaining comprehensive audit trails for every operation.
Goal-oriented execution: Pursues defined outcomes through multi-step planning, with each action authorized against identity policies and access permissions to ensure operations remain within approved scope and organizational boundaries.
Adaptive learning with persistent context: Learns from outcomes and maintains context across sessions, requiring secure handling of authentication tokens and sensitive information throughout the agent lifecycle to prevent unauthorized access.
Memory and state management: Retains operational history and user preferences through encrypted storage, confined to the agent’s delegated identity, ensuring continuity while protecting against unauthorized access to accumulated knowledge and operational data.
These capabilities enable agentic AI to handle complex workflows traditionally requiring human oversight, but they depend on identity infrastructure that can verify, authorize, and audit every action. This includes dynamic, policy-based identity governance and identity posture management with continuous validation for non-human identities.
Types of AI agents and their identity requirements:
Reactive agents: Respond directly to environmental stimuli with just-in-time access to resources, requiring temporary but properly scoped credentials that minimize exposure windows.
Deliberative agents: Use reasoning and planning to predict the consequences of their actions, requiring persistent but strictly controlled identities with comprehensive audit trails and behavior monitoring.
Hybrid agents: Combine reactive and deliberative models, requiring adaptive access controls that can shift between immediate response and planned execution modes based on operational context.
Multi-agent systems: Networks of agents that collaborate through secure delegation chains and verifiable credentials, each with its own identity but operating under a unified governance model that maintains trust across agent interactions.
Identity-aware delegation is essential to avoid improper impersonation or privilege escalation. Each agent must act under explicitly scoped, auditable, and time-bound credentials, not shared or persistent tokens.
Agentic AI architecture
Building enterprise-ready agentic AI requires a security-first architecture with identity verification and access controls woven into every component rather than added as an afterthought.
Embed identity in every layer
Core components with integrated identity:
Perception layer: Ingests data from APIs, databases, and real-time sources with authentication verification and data provenance validation, ensuring only authorized information enters the system and maintaining data integrity throughout processing.
Planning engine: Breaks down goals into executable tasks while consulting authorization policies in real time, ensuring agents only plan actions they have permission to execute within their designated role boundaries and organizational constraints.
Action engine: Executes tasks through authenticated API calls with properly limited access tokens, maintaining least-privilege principles and comprehensive logging for every operation to ensure full accountability.
Memory and context management: Maintains a persistent state with secure credential handling and encrypted storage, enabling operational continuity while protecting sensitive data and authentication materials from unauthorized access.
This architecture supports identity-native operations by integrating authentication mechanisms, policy based access control (PBAC), continuous authorization validation, and verifiable delegation into every component.
Agentic AI use cases
Agentic AI is transforming industries by automating complex decision-making and task execution while maintaining security and compliance standards.
Use case examples:
Security operations: AI agents autonomously investigate alerts by correlating identity signals across systems, initiating containment workflows with granular permissions, and maintaining comprehensive audit trails. These agents operate with just-in-time elevated privileges that minimize security exposure while enabling rapid threat response.
Customer support: Agents provide end-to-end issue resolution through authenticated access to customer accounts. Identity verification is performed at each interaction step, and full traceability of all actions taken on behalf of users ensures both efficiency and accountability.
IT automation: Agents manage deployment pipelines with temporary elevated privileges, monitor system health through properly scoped access, and scale infrastructure using securely delegated authority that maintains separation of duties and change control processes.
Financial services: Personal finance agents make portfolio adjustments through verified delegation chains, ensuring every transaction has proper authorization and non-repudiation while maintaining strict regulatory compliance and audit requirements.
Access lifecycle management: Agents orchestrate user onboarding, permission adjustments, and offboarding processes with cryptographically verified authority, enforcing least-privilege principles through continuous evaluation of role requirements, user behavior, and risk profiles.
Identity-centered applications:
Continuous authentication: Agents dynamically monitor user behavioral patterns and adapt authentication requirements, operating with granular permissions that allow monitoring without overreach or privacy violations.
Intelligent access governance: AI systems analyze access patterns through securely delegated admin privileges, identifying role optimizations while maintaining separation of duties and eliminating excessive privileges.
Threat detection: Agents identify anomalous access patterns through properly authenticated monitoring channels, correlating identity signals that would otherwise remain siloed across different security tools.
Cross-platform identity orchestration: Agents maintain consistent identity attributes across systems through secured privileged access and policy-aligned synchronization, enabling unified identity management across cloud, SaaS, and on-prem systems.
These implementations demonstrate how agentic AI frameworks can transform business processes while maintaining the security posture and governance standards essential for enterprise adoption.
Agentic AI examples
AutoGPT/BabyAGI: Open-source frameworks that create autonomous task-planning agents using LLMs, but lack the identity governance needed for enterprise production environments.
Enterprise copilots: Agents embedded in productivity suites that operate through delegated user authority with scoped access tokens, allowing them to schedule meetings and send emails while maintaining clear accountability and audit trails.
Security assistants: Agents that analyze threat intelligence through properly authenticated API access, correlate identity signals with appropriate permissions, and initiate incident response through cryptographically verified authority chains.
E-commerce assistants: Agents that guide users through product discovery and checkout while respecting identity boundaries and securely handle customer information throughout the transaction process.
Identity-governed implementations:
Zero Trust AI agents: Architectures requiring continuous authentication for every agent action, with no persistent trust assumptions and continuous validation of identity assertions throughout operations.
Workforce management agents: Systems that monitor employee lifecycle events through properly authenticated HR integrations and automatically adjust access rights through verified administrative channels with full audit trails.
Customer service agents: Services facilitating secure authentication across complex customer journeys, with agents operating under delegated but limited authority to improve user experience (UX) without compromising security boundaries.
Developer assistance agents: AI systems that integrate with development environments and CI/CD pipelines, operating with repository-specific permissions and code review authority while maintaining audit trails for all automated commits, deployments, and infrastructure changes.
Benefits of agentic AI
Agentic AI delivers transformative value when properly secured and implemented with comprehensive identity governance:
Scalable operations: Handle repetitive tasks at scale while maintaining individual authentication and access controls for each operation, enabling mass customization without compromising security or creating shared credential vulnerabilities.
Accelerated decision-making: Make autonomous decisions with pre-verified access rights, accelerating processes while ensuring all actions remain within approved security boundaries and organizational policies.
Consistent execution: Perform tasks uniformly through standardized identity protocols, reducing human error and security exceptions while maintaining regulatory compliance and audit requirements across all operations.
Adaptive efficiency: Learn from experience through securely maintained context, evolving to handle increasingly complex tasks while minimizing privilege scope and avoiding lateral risk expansion.
Enhanced security posture: Eliminate shared credential risks through personalized, traceable access paths while enabling real-time threat response through predefined but flexible authorization frameworks that adapt to changing risk conditions.
Combining autonomous capability with identity-driven security creates opportunities for innovation and risk mitigation that weren't possible with previous automation approaches. This enables organizations to scale operations while strengthening their security posture.
Challenges and risks of agentic AI
As organizations move quickly to deploy agentic AI, the challenges of securing systems and controlling non-human identity sprawl are often overlooked:
Delegation and control: Autonomous systems require precisely defined access boundaries with authentication and authorization enforced at every interaction point. Improper delegation or token reuse can lead to privilege escalation, unauthorized access to sensitive resources, or compliance violations.
Operational transparency: Understanding agent decisions requires comprehensive identity-linked audit trails that trace every operation to its authorization source. This requires balancing transparency requirements with system performance and operational efficiency.
Credential security: Protecting the access tokens, API keys, and service accounts that agents use demands advanced secret management, automated rotation mechanisms, and just-in-time provisioning to minimize exposure windows and prevent credential theft.
Regulatory compliance: Agents must navigate complex regulatory constraints through identity-aware policies that adapt to jurisdictional requirements while maintaining consistent security postures across different operational environments.
Scale and complexity: As organizations deploy hundreds or thousands of agents, managing their individual identities, permissions, and interactions becomes exponentially complex without proper identity governance frameworks and automated management tools.
Prompt injection and AI-specific attacks: Malicious actors are developing sophisticated techniques to manipulate agent behavior through crafted inputs, making identity boundaries and privilege limitations essential safeguards against AI-targeted exploitation.
Integrating agentic AI identities in the identity security fabric ensures continuous controls and Zero Trust principles extend to autonomous agents, just as they do to human users.
Agentic vs. generative AI
Capability | Generative AI | Agentic AI |
Core function | Generate content with limited system access | Take autonomous action through authenticated channels with delegated authority |
Interaction style | Prompt and respond with minimal persistence | Iterative execution with maintained identity context and session continuity |
Examples | ChatGPT (content generation), Midjourney (image creation) | AutoGPT (task automation), LangChain agents (workflow orchestration) |
Autonomy | Limited to generation with minimal system interaction | Highly autonomous capability requiring comprehensive identity governance and access controls |
Memory | Stateless or limited context, minimal identity requirements | Stateful with persistent identity association, secure credential management, and encrypted context storage |
Identity needs | Basic authentication, typically read-only access | Comprehensive identity lifecycle management, delegation chains, fine-grained access control, continuous authorization validation, and continuous monitoring and adaptive controls |
Fundamental identity differences:
Access patterns: Generative AI typically requires limited, read-only access through simple authentication, while agentic AI needs granular, contextual access with proper authorization validation for each action and comprehensive audit trails.
Session management: Generative AI often operates with static permissions throughout a session, while agentic AI requires dynamic identity contexts that adapt as tasks progress and security conditions change.
Delegation requirements: Generative AI has limited need for delegation chains or impersonation capabilities, while agentic AI must operate through clear, auditable delegation paths with proper authorization and time-bound permissions.
LLMs and identity in agentic AI
Many agentic systems rely on large language models (LLMs) as core reasoning engines, but LLMs must be integrated with comprehensive identity frameworks to operate securely in enterprise environments.
While LLMs provide powerful reasoning capabilities, they lack inherent security awareness. They can exhibit unpredictable behavior and are vulnerable to potential prompt injection attacks and data leakage risks, making identity guardrails essential for enterprise deployment.
When LLMs are given autonomous capabilities beyond conversation, they must operate within a comprehensive identity framework that provides policy enforcement, delegation chain management, comprehensive auditing, and precise access boundaries.
Enterprise LLM integration requires foundational identity capabilities:
User authentication for agent initiation,
Secure token management for API interactions,
Asynchronous authorization for long-running tasks
Fine-grained permissions for retrieval-augmented generation (RAG) operations
Modern identity platforms enable secure LLM integration through fine-grained authorization policies, secure credential issuance for properly scoped operations, contextual and risk-based authentication for sensitive actions, and comprehensive audit capabilities that maintain non-repudiation throughout agent activities.
The future of agentic AI
AI models are evolving rapidly from experimental to essential, reshaping business operations across industries.
Key developments on the horizon include:
Identity-native frameworks: Platforms with identity governance built into core architecture rather than retrofitted as security add-ons, enabling secure-by-design agent deployment and management.
Cross-organizational collaboration protocols: Standardized identity frameworks enabling secure agent operations across enterprise boundaries while maintaining trust, accountability, and audit trails.
Behavioral trust and continuous verification: Systems that continuously validate agent actions against expected patterns, with identity-linked anomaly detection and adaptive authorization that responds to changing risk conditions in real-time.
Regulatory compliance standards: Industry protocols designed explicitly for agent delegation, federation, and dynamic authorization requirements that address the unique challenges of autonomous systems, including AI governance frameworks like the EU AI Act and emerging NIST guidelines.
Decentralized credential systems: Technologies allowing agents to prove delegated authority through distributed verification without relying solely on centralized authentication services.
Ethical governance frameworks: Standardized approaches ensuring agent actions align with organizational values through identity-linked policy enforcement and governance mechanisms.
Quantum-resistant cryptography: As quantum computing advances, identity systems for long-lived AI agents must incorporate post-quantum cryptographic standards to maintain security against future computational threats.
As AI agents increasingly represent humans and organizations in digital transactions, every action must be verified, properly authorized, and fully traceable through identity frameworks that scale with autonomy and complexity.
Secure autonomous AI starts with identity
Identity management isn’t just a security consideration. It’s the foundation that makes autonomous agent deployment possible. Discover how Okta can enable secure agentic AI at scale across your organization.
