An Agentic Architecture Review is a structured assessment of how an AI system is designed to make decisions, use tools, and interact with identities and data. It examines control boundaries, NHI governance, authentication, authorization, logging, safety, and failure modes across agent workflows, especially where autonomous actions can affect systems or secrets.
What an Agentic Architecture Review Examines
An agentic architecture review looks at the system as a decision-making chain, not just as a model. It asks whether the agent’s objectives, tool access, control boundaries, and data flows are aligned so autonomy does not become uncontrolled authority.
That means reviewing where the agent is allowed to act on its own, where human approval is required, and where a request must be constrained by policy before it reaches a tool, API, or downstream system. A sound review treats those limits as part of the architecture itself, not as an afterthought.
Core Security Questions in the Review
The first question is whether the agent can only do what its role and task require. That includes checking whether it can reach credentials, invoke sensitive tools, reuse prior context, or cross trust boundaries that were never meant to be automatic.
The next question is whether identity and authorization are enforced at the right points in the workflow. For agentic systems, the most important failure is often not model quality, but a mismatch between what the agent can decide and what it is actually permitted to execute.
Logging, Safety, and Failure Modes
Reviewers also need to understand whether the system can explain what happened after the fact. Logging, attribution, and auditability matter because autonomous actions are harder to reconstruct when a workflow spans prompts, tools, memory, and external services.
Safety analysis should cover misrouting, tool misuse, prompt injection, delegated access abuse, and cascading failures across multi-step workflows. If the system cannot detect when an agent is drifting, overreaching, or being manipulated, the architecture is brittle even if each component looks secure in isolation.
Controls such as AI Agent Observability, Audit and Incident Response Guide help frame the logging and response questions that this review should answer.
Why This Review Matters for Autonomy and Control
An agentic architecture review is ultimately about deciding how much trust the system deserves at each step. The more an agent can plan, route, and act, the more the architecture needs explicit boundaries around authentication, authorization, human approval, and secret handling.
That is why mature reviews compare the intended autonomy level with the actual control plane. Where those two drift apart, organisations often discover overprivileged agents, weak oversight, or hidden dependencies only after an incident or unsafe action.
For a deeper treatment of design patterns around access and authority, see AI Agent Authorisation Guide and Zero Trust for AI Agents.
Risk and Threat Considerations
Agentic architectures create concentrated risk when a single compromise, bad instruction, or excessive permission can trigger action across multiple tools, systems, or data stores. The danger is not only malicious abuse, but also unbounded automation that turns one error into a wider security event.
Failure mechanism: An attacker, or a malformed workflow, can steer the agent into misusing delegated authority, exposing secrets, or taking actions that bypass intended human review and policy checks.
Impact: The result can include unauthorized access, data exposure, unsafe system changes, persistence through retained access, and a difficult post-incident investigation because the activity was spread across multiple autonomous steps.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agentic reviews center on authority boundaries and delegated action rights. |
| ASI02 — Tool Misuse | The review assesses whether tools can be invoked safely and only within intended boundaries. | |
| ASI06 — Memory & Context Poisoning | Architecture reviews must consider whether context can be polluted into unsafe decisions. | |
| Recommendation — Limit agent authority to the minimum needed for each action and verify delegated access paths. Constrain tool access and inspect tool invocation paths for unsafe or unintended use. Isolate agent context sources and validate stored memory before it influences actions. | ||
| NIST AI RMF | Govern | Agentic architecture reviews are a governance exercise over AI system design and accountability. |
| Recommendation — Establish AI governance for autonomy, oversight, and documented responsibility across the system. | ||
| NIST SP 800-53 Rev 5 | AU-2 — Audit Events | Logging and traceability are central to understanding agent actions and failures. |
| Recommendation — Define audit events that capture agent decisions, tool use, and privileged actions. | ||
Practitioner Guidance
What to watch for: Review the architecture where autonomy and privilege meet, especially at tool boundaries, identity handoffs, and approval gates. If the agent can still complete a sensitive task after the control layer fails open, the review has not reached the real risk surface.
Governance implication: Ownership should be explicit for the agent workflow, the credentials it can use, the logs it must produce, and the conditions under which it must stop. That accountability is what turns an “AI feature” into a governable system.
Practitioner takeaway: The best agentic architecture reviews test whether the system can be safely denied, not just whether it can successfully act.
Useful reference points for broader threat modelling and governance include Threat Modelling AI Agents and the OWASP Agentic AI Top 10.
Related resources from NHI Mgmt Group
- What should organisations review before adopting agentic API access controls?
- What breaks when agentic AI is managed with human-style review cycles?
- What should IAM teams review when building a healthcare reference architecture?
- How should security teams prepare identity data for agentic audit review?
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Reviewed and updated by the NHIMG editorial team on October 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org