Start with a task contract
Before selecting an agent framework, write down the task a user needs completed and the evidence that would prove completion. Specify input records, permitted actions, expected output and the person who reviews exceptions. A support agent that prepares a response has a different execution contract from one that changes an order.
Define a baseline using the existing process or a fixed workflow. An agent is useful when the next action depends on what it discovers, but that flexibility adds opportunities for errors. Anthropic’s engineering guide on effective agents distinguishes workflows from dynamic agents and discusses the cost and latency tradeoff. Its patterns are conceptual guidance; check current documentation for the implementation you choose.
Implement a narrow vertical slice
Begin with one complete path from an authorized request to a reviewable result. For an illustrative support workflow, read an authorized ticket, retrieve the relevant policy and prepare a reply draft. Record which policy version supports the draft. Test the path before adding ticket updates or another system.
Describe each tool’s inputs, output schema, error states and side effects. Validate arguments in application code, and enforce resource permissions at execution time. A model-proposed ticket identifier is not proof that the user may access that ticket. Keep read operations, draft creation and publication separate so the application can apply the right review rule.
Track execution state explicitly
Store the task identifier, current step, tool attempt and authoritative result. Distinguish a planned action from an attempted action and a confirmed change. When a request times out, query the receiving system before retrying where possible. An ambiguous response may mean the change completed even though the agent did not receive confirmation.
Define duplicate handling with the receiving API’s actual semantics. Persist enough state to resume a stopped task without repeating a completed write. Record the model, prompt and tool-contract versions used for the run. Limit stored data to what operators need to investigate and recover the task, with agreed access and retention.
Build a failure-oriented evaluation set
Include ordinary tasks alongside missing records, stale policies, ambiguous requests, revoked permissions and unavailable tools. Add retrieved documents that contain misleading instructions. Test that application controls prevent unauthorized actions even when the model proposes them. Keep development examples separate from the final evaluation set.
Judge the action trace as well as the final response. A fluent answer can conceal a missed update or the wrong record. Measure verified task completion, incorrect actions, unsupported statements, review corrections, time to completion and cost per completed task. Record unresolved cases and compare with the baseline under the same conditions.
Release with bounded authority
Run the agent in observation or draft mode before enabling writes. Agree who approves publication, which actions require escalation and how the task stops after repeated failures or budget exhaustion. Start with a bounded group of users and tasks, with a clear fallback to the existing process.
Review tool failures, user corrections and permission incidents after launch. Re-run relevant evaluation when a model, policy collection, connector or intended task changes. Rolling back code does not automatically undo a completed external action; provide a reconciliation procedure and an accountable operator.
Agree the delivery artifacts
A project handover should include the task contract, tool schemas, data-flow and permission map, evaluation report, versioned configuration and operating runbook. Scope integration, evaluation, hosting and support costs separately. Framework choice is one part of that contract, rather than evidence that the finished system is reliable.
Use the agent architecture guide for design tradeoffs and the MLOps guide for release operations. Explore AI implementation planning or discuss a scoped agent project with TensorBlue.