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18/08/2026 2:59 pm
TL;DR: Agentic AI frameworks now shape how agents plan, call tools, and retain state, but TruFoundry’s comparison shows that production readiness still depends on governance, observability, and policy enforcement rather than orchestration alone. The real risk is unmanaged tool access and audit gaps across frameworks, which makes gateway-layer controls the deciding factor for enterprise AI teams.
NHIMG editorial — based on content published by TruFoundry: Best Agentic AI Frameworks for 2026 compared for enterprise AI teams
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.
Q: Why do AI agents create new risk in non-human identity management?
A: AI agents create risk because they operate as software identities with delegated authority, but many organisations do not track them with the same discipline applied to users or service accounts.
Q: What breaks when MCP access is not centrally enforced?
A: When MCP access is not centrally enforced, agents can bypass the sanctioned protocol and reach the same data through alternative connectors or direct application paths.
Practitioner guidance
- Define a single governance layer above all agent frameworks Centralise model access, MCP tool permissions, logging, and budget limits in one policy boundary so framework choice does not change security posture.
- Classify agent tool use as privileged access Map every tool, API, and database an agent can reach to an approved entitlement and review it like any other high-risk access path.
- Require auditable checkpoints for long-running workflows Preserve execution traces, state transitions, and handoff decisions so security and compliance teams can reconstruct agent behaviour after an incident.
What's in the full article
TruFoundry's full comparison covers the operational detail this post intentionally leaves for the source:
- Framework-by-framework implementation notes for LangGraph, CrewAI, AutoGen, Google ADK, OpenAI Agents SDK, LlamaIndex, and Semantic Kernel
- The platform-specific governance functions TrueFoundry maps to model access, tool permissions, tracing, and budget enforcement
- Practical guidance on when a framework's orchestration model becomes a liability for portability or auditability
- How the gateway layer is positioned to sit above multiple agent runtimes without forcing a single development stack
👉 Read TruFoundry's comparison of the top agentic AI frameworks in 2026 →
AI agent frameworks in 2026: what governance gaps teams miss?
Explore further
18/08/2026 3:39 pm
Framework selection is now a governance decision, not only an engineering preference. The article makes clear that orchestration style affects durability, recovery, and tool behaviour, but the security impact is the governance layer above it. That matters because agentic systems can cross system boundaries quickly once they can plan, call tools, and retain state. For identity teams, the framework question now sits alongside privilege, auditability, and control ownership.
A question worth separating out:
Q: What should organisations review before choosing an agent framework?
A: They should review orchestration style, state persistence, recovery behaviour, observability, portability, and how governance will be enforced outside the framework. A good prototype can still be a poor production choice if it makes audit, control, or provider flexibility harder later.
👉 Read our full editorial: AI agent framework choice is now an identity governance decision
