Agentic artificial intelligence (AI) frameworks are development platforms for building autonomous AI systems that can plan, act, and adapt with minimal human supervision. Unlike traditional automation tools that follow scripted workflows, agentic AI frameworks equip software agents with the ability to reason, make independent decisions, and continually improve over time.
For enterprises, this autonomy creates both opportunity and risk. Agentic AI can accelerate operations, scale decision-making, and unlock new use cases. But without robust identity-first security, governance, and compliance controls built in, these frameworks may introduce unmanaged non-human identities, compliance gaps, and attack vectors that traditional tools weren’t designed to handle. Managing agentic AI security best practices requires centralized identity and enterprise governance.
What is an agentic AI framework?
An agentic AI framework is a structured environment for building and managing autonomous AI agents, providing:
Perception: Interpreting inputs from users, data, or environments
Planning: Creating strategies to achieve goals
Action: Executing tasks through APIs, applications, or external systems
Memory: Retaining context to improve decision-making
There are multiple types of agentic AI, from single-agent frameworks to collaborative multi-agent systems with varying scalability, governance, and security capabilities.
Examples of autonomous AI frameworks for building agentic AI systems include:
LangGraph: Orchestration for multi-agent workflows, built on LangChain
CrewAI: Collaborative multi-agent execution (currently early-stage)
Azure AI Foundry: Enterprise-scale AI development hub
OpenAI Agents SDK: Production-ready multi-agent orchestration framework (evolution of the discontinued Swarm project)
Some platforms also introduce an agentic LLM framework, which extends large language models with autonomous reasoning, planning, and identity-aware security controls.
The identity security imperative for framework selection
Choosing an agentic AI framework fundamentally determines an organization’s identity security posture. Traditional software evaluations often focus only on features and performance, but framework selection must prioritize identity governance capabilities from day one.
Modern enterprises face an exponential growth in non-human identities, with some environments showing ratios of 50:1 or higher compared to human users. Agentic AI frameworks accelerate this trend, potentially creating thousands of temporary agent identities that operate across cloud, SaaS, and hybrid environments without traditional oversight mechanisms. Since most frameworks today still rely on API key-based authentication with minimal identity and access management (IAM) maturity, an enterprise identity security fabric becomes the essential bridge to unify policy enforcement and maintain control.
The identity-first approach to framework selection addresses three critical enterprise concerns:
Regulatory compliance alignment
With the EU AI Act's Article 14 requiring effective human oversight and the NIST AI Risk Management Framework emphasizing governance, frameworks should provide built-in compliance capabilities to support regulatory alignment, while acknowledging that some customization may still be necessary as regulations evolve.
Operational risk mitigation
Identity sprawl is a rapidly growing risk in enterprise environments, particularly as the adoption of agentic AI expands. Frameworks that lack native identity governance create unmonitored access points that attackers increasingly exploit.
Future-proofing investments
As AI capabilities evolve, frameworks with robust identity foundations can adapt to new regulatory requirements and security standards without requiring complete architectural overhauls.
Framework architecture and identity considerations
Many agentic frameworks, including large language model (LLM)-based implementations, rely on layered structures with perception, planning, action, and memory to manage identity and security.
Perception Layer
Purpose: Interpret prompts, data, and events
Identity and security implications: Authenticate data sources and verify input integrity
OWASP LLM Top 10 Alignment: LLM01:2025 Prompt Injection and LLM02:2025 Sensitive Information Disclosure
Planning Layer
Purpose: Generate strategies and select AI agent development tools
Identity and security implications: Control agent privileges, enforce role-based access control (RBAC), and attribute-based access control (ABAC)
OWASP LLM Top 10 Alignment: LLM06:2025 Excessive Agency and LLM05:2025 Improper Output Handling
Action Layer
Purpose: Execute APIs and system commands
Identity and security implications: Secure short-lived credentials and audit every action
OWASP LLM Top 10 Alignment: LLM03:2025 Supply Chain, LLM02:2025 Sensitive Information Disclosure, and LLM10:2025 Unbounded Consumption
Memory Layer
Purpose: Retain state and context
Identity and security implications: Encrypt stored data and apply data retention policies
OWASP LLM Top 10 Alignment: LLM08:2025 Vector and Embedding Weaknesses and LLM06:2025 Excessive Agency
Each layer expands the non-human identity surface area. Securing frameworks means enforcing least privilege, continuous monitoring, and governance at every layer. Centralized identity management, delivered through an identity-first strategy, reduces unmonitored attack surfaces and sustains Zero Trust.
Enterprise framework evaluation
When evaluating frameworks for enterprise deployment, security and identity teams should assess AI agent security capabilities across five essential dimensions:
Identity integration maturity
Native IAM support: Frameworks should integrate directly with enterprise identity providers without requiring custom middleware
Credential management: Evaluate how frameworks handle secret rotation, just-in-time access, and credential lifecycle management. Strong practices here are foundational to agentic AI governance and compliance
Delegation chains: Look for frameworks that maintain clear audit trails showing which human user initiated agent actions
Security architecture alignment
Zero Trust compatibility: Frameworks should assume Zero Trust between agents and require continuous verification
Network segmentation: Agents should operate within micro-segmented environments (isolated network zones) without compromising functionality
Threat modeling: Prioritize frameworks assessed against AI-specific threat models like OWASP LLM Top 10 and MITRE ATLAS. Mapping against these frameworks ensures alignment with enterprise agentic AI security requirements
Governance and compliance readiness
Regulatory alignment: Frameworks must support GDPR, HIPAA, SOC 2, and emerging AI-specific regulations as part of comprehensive enterprise AI governance
Audit capabilities: All agent decisions and actions should be reconstructible for compliance reporting
Policy enforcement: Evaluate how frameworks implement and enforce organizational AI governance policies
Operational resilience
Monitoring and observability: Frameworks should provide comprehensive tools for real-time agent behavior monitoring
Incident response: Problematic agents must be quickly identified, contained, and remediated
Rollback capabilities: Frameworks should safely revert agent configurations or actions when issues arise
Scale and performance considerations
Multi-cloud support: Agents should operate consistently across different cloud environments
Resource optimization: Frameworks must manage computational resources and prevent runaway agent behavior
Concurrent operations: Clear limits should exist for simultaneous agent operations with proper enforcement mechanisms
Security-first design patterns for frameworks
While frameworks vary, most rely on common patterns. Each has unique security considerations that differ slightly when working with an agentic LLM framework versus single-agent deployments:
Tool Use
Pattern: Agents call APIs/tools for execution
Risk: Over-permissioned credentials
Best practice: Short-lived, just-in-time access managed through enterprise IAM
Reflection and self-critique
Pattern: Agents review outputs before execution
Risk: If compromised, false “self-approval”
Best practice: Independent validation layers; external guardrails
Multi-agent collaboration
Pattern: Teams of agents coordinate
Risk: Privilege escalation across agents
Best practice: Isolate agent identities, enforce continuous authentication, and per-request verification
ReAct (Reason and act)
Pattern: Agents iteratively reason and execute
Risk: Looping actions, runaway execution
Best practice: Execution caps, auditable checkpoints
Frameworks that fail to embed these safeguards risk exposing enterprises to uncontrolled agent behavior.
Framework selection criteria for enterprises
When evaluating frameworks, enterprises should prioritize:
Identity integration: Support for enterprise IAM, RBAC/ABAC, and non-human identity management
Security architecture: Compatibility with Zero Trust and segmented access
Governance and compliance: Alignment with GDPR, HIPAA, EU AI Act, NIST AI RMF
Operational resilience: Incident response, observability, and rollback capabilities
Frameworks should be measured by how well they align with the enterprise security strategy, rather than solely on their features.
Regulatory compliance and governance considerations
The regulatory landscape for agentic AI is evolving rapidly. Frameworks needing to address multiple overlapping requirements:
EU AI Act compliance
Article 14 of the EU AI Act requires demonstrable human oversight, with high-risk system requirements phased in from February 2025. Identity-centric approaches embed oversight into every agent interaction through verified human delegation chains and immutable decision trails.
Frameworks must demonstrate:
Clear human intervention points in agent decision-making
Transparent audit trails showing human authorization for autonomous actions
Mechanisms for humans to interrupt or override agent behavior
Industry-specific requirements
Frameworks must address industry-specific requirements, including:
HIPAA for healthcare data privacy
SOX/PCI DSS for financial audit trails
FedRAMP security controls for government environments
Data sovereignty and cross-border considerations
Organizations operating across multiple jurisdictions must ensure frameworks can:
Implement data residency requirements for agent operations
Maintain separate compliance postures for different regulatory environments
Provide jurisdiction-specific audit capabilities and data handling procedures
Emerging security challenges
As adoption accelerates, enterprises face:
Non-human identity sprawl: Thousands of unmanaged agent and service accounts
Cross-framework interoperability: Hard to enforce uniform policies across LangGraph, Semantic Kernel, and others
AI-specific attack vectors: Prompt injection, model manipulation, and supply chain risks (per OWASP LLM01:2025)
Regulatory complexity: Compliance varies by geographical location and industry, and requires consistent audit trails
Best practices for secure deployment
AI agent security fundamentals:
Identity-first design
Treat every agent as a first-class identity
Apply automated identity lifecycle management: provision, validate, rotate, and decommission agent identities
Defense in depth
Enforce Zero Trust: never assume agent-to-agent trust
Segment agent privileges, use micro-permissions
Governance by default
Require policy-based access controls
Align with NIST AI RMF and EU AI Act guardrails
Observability and auditability
Centralize logging of all agent actions
Automate anomaly detection (impossible travel, credential misuse)
Step-by-step team checklist
Register every agent with enterprise IAM
Issue short-lived credentials through a secure broker
Apply RBAC/ABAC to restrict tool access
Continuously rotate and revoke unused identities
Monitor actions with anomaly detection and alerts
Future trends in agentic frameworks
Enterprises should prepare for:
Agent marketplaces
Cross-framework interoperability
Agent supply chain security
FAQ
How are agentic AI frameworks different from automation tools?
Frameworks support reasoning, planning, and adaptation, while automation tools follow static workflows.
How do you manage short-lived agent credentials?
Issue temporary credentials through IAM, enforce rotation, and log all usage.
Can frameworks integrate with enterprise IAM?
Most rely on API keys. Enterprise-grade use requires a centralized identity security fabric to unify IAM across multi-cloud and hybrid environments.
What compliance rules apply to agentic AI?
Frameworks must align with GDPR, HIPAA, SOC 2, and new AI regulations, including the EU AI Act.
How do you monitor and audit autonomous agents?
Logging, anomaly detection, and immutable audit trails support oversight of autonomous agents, helping enterprises maintain governance and compliance.
How do frameworks enable Zero Trust?
A Zero Trust approach enforces per-agent authentication, micro-segmentation, and continuous verification across all agent operations.
Ready to secure your agentic AI deployment?
Agentic AI frameworks are rapidly evolving into the backbone of enterprise AI adoption. They provide structure for autonomous agents, but introduce new risks around identity, security, and compliance.
An identity-first strategy is the only way to manage this complexity at scale. Identity serves as the control plane for agentic AI, providing unified visibility and lifecycle management across all agent operations. By securing non-human identities, enforcing Zero Trust, and embedding governance into every layer, enterprises can confidently deploy frameworks that make agentic AI transformative.
Discover how the Okta Platform secures agentic AI by managing non-human identity lifecycles, delivering interoperability and enterprise IAM capabilities, and providing the foundation for safe, scalable AI adoption.
