Agentic AI and MCP Integration

Decide whether an agent or deterministic workflow suits a task.

Evidence: A design states allowed actions, termination and human responsibility.

Represent task state and separate proposed steps from executed steps.

Evidence: A state trace prevents repetition after a completed action.

Define typed inputs, outputs and explicit failure responses.

Evidence: Contract tests reject malformed and unauthorised tool requests.

Map client, server, tools and resources in a controlled integration.

Evidence: A sequence explanation identifies the trust boundary and credentials owner.

Expose a read-only mock catalogue tool using a pinned SDK.

Evidence: The client obtains only authorised fields and handles an unavailable server.

Bind tool access to the authenticated user and task entitlement.

Evidence: Cross-user calls fail even when a model requests them.

Choose task-local state and redact persistent notes.

Evidence: A retention test removes disallowed sensitive content.

Pause before a simulated consequential action and resume with a decision.

Evidence: Rejecting approval results in no action; approval is tied to exact arguments.

Recover from timeouts without repeating a committed action.

Evidence: A retry test produces one durable effect and a complete trace.

Assess tool selection, argument accuracy and final outcomes.

Evidence: A scenario suite distinguishes reasoning errors from tool failures.

Compare one agent with delegated roles using the same tasks.

Evidence: A measured comparison justifies complexity or retains the simpler design.

Record sanitised traces, budget limits and stop conditions.

Evidence: A demonstrable runaway loop stops within the configured limit.