Short answer: A governed AI agent connects a model to approved context and tools through explicit policies. It records important state, validates actions, and gives people a clear way to review, override, or stop work.
The context layer
Context may include approved documents, records, policies, event data, and user permissions. Retrieval should be narrow, attributable, and designed around the decision rather than dumping every available document into a prompt.
The action layer
Tools are explicit capabilities such as searching an approved system, creating a draft, or preparing a record update. Each tool needs an input contract, access policy, error handling, and an audit trail.
The control layer
Policies, deterministic checks, confidence thresholds, approval queues, and monitoring make the system governable. A human reviewer should see enough context to make a meaningful decision, not merely accept a black-box recommendation.
Memory is not a bucket for every conversation
Working memory holds the current task: the request, retrieved records, intermediate tool results, and state needed to finish safely. Longer-lived memory should be deliberately designed: a durable customer preference or approved workflow state belongs in an authoritative system with retention and access rules, not in an uncontrolled prompt history.
A useful architecture makes it clear which value came from which system, when it was retrieved, and whether it can be reused. This avoids a common failure mode where an old, unverified conversation detail is treated as current business fact.
Make human review meaningful
A reviewer needs the request, relevant evidence, the proposed action, and an explanation of why the system chose that route. A button labelled “approve” without context turns human review into a rubber stamp. Build queues around decisions where a person can genuinely add judgement.
Review feedback should feed a controlled improvement loop. Categorise the failure—missing data, poor retrieval, unclear policy, tool error, or ambiguous request—then change the right layer. Do not simply add more instructions to the prompt and assume the problem is solved.
Architecture should make responsibility visible
Good architecture is less about drawing boxes and more about making responsibility visible. One layer owns source retrieval, another owns tool access, another enforces policy, and a reviewer owns consequential approval. When those responsibilities are mixed together in a single prompt or opaque service, it becomes difficult to tell why the system behaved as it did or what should be changed after an error.
The same principle applies to observability. Logs should support a real question: Which source was used? Which tool was called? What was blocked? Who overrode the recommendation? How often did a handoff occur? A useful audit trail helps operators improve the system and helps reviewers distinguish an unavoidable ambiguity from a preventable engineering problem.
Trace a request through the architecture
A request should enter the system with an identity, a purpose, and a defined set of permissions. The context layer retrieves only the records this user and task may access. The reasoning layer decides whether the request can be answered, needs a tool, or needs a handoff. The action layer then calls an approved capability with structured inputs. At every stage, the system should be able to state what it used and what it decided not to use.
The architecture should resist the temptation to store everything as free-form conversation history. Customer records belong in the system that governs them; policies belong in an approved document collection; durable workflow state belongs where it can be reviewed and corrected. The agent can assemble these pieces for a task, but it should not become an ungoverned shadow database.
Human review completes the loop. A reviewer may approve a prepared action, correct a source mismatch, or tell the system that the request was outside scope. That feedback should be captured as a specific signal, not as an unexplained thumbs-down. Specific feedback helps the team improve retrieval, rules, tools, or the interface in the right place.