What you will learn
- How to choose a bounded agent use case.
- How tools, context, approvals and guardrails fit together.
- How to evaluate quality before and after release.
- Which artifacts make an agent project defensible in an interview.
1. Start with a decision, not a model
Choose one user, one measurable job and a clear boundary. “Answer employee policy questions and create an HR case when evidence is insufficient” is testable. “Build an HR super-agent” is not. Write the success condition, prohibited actions, escalation rule and data boundary before choosing a framework.
2. Turn the job into an explicit workflow
List the information the agent needs, the tools it may call and the state that must survive between steps. Prefer a deterministic workflow when the route is already known. Use an agent when the system genuinely needs to select among tools or decide the next step from changing context.
Define the objective, constraints and completion criteria.
Use narrow contracts, validated inputs and typed outputs.
Transfer work only with the context the next owner needs.
Persist only what the workflow requires and policy allows.
3. Treat context as governed input
More context is not automatically better. Retrieval should return current, permission-appropriate evidence with enough metadata to verify the answer. Tool responses, conversation state and retrieved documents all need size, sensitivity and freshness rules. If MCP is used, each server is a trust boundary with its own authorization and audit needs.
4. Put controls around consequential actions
- Validate tool inputs on the server, not only in the prompt.
- Use least-privilege identities and separate read from write operations.
- Require human approval for high-impact or irreversible actions.
- Prevent instructions in retrieved content from changing system policy.
- Record who requested, approved and executed each action.
- Define a safe fallback when evidence or a dependency is unavailable.
5. Evaluate the behavior, not the demo
Build an evaluation set from normal tasks, edge cases, policy refusals, tool failures and adversarial inputs. Measure task completion, evidence quality, correct tool selection, argument validity, policy adherence, handoff quality, latency and cost. A change should not ship when it improves a showcase answer but breaks a critical case.
| Layer | What to test | Evidence |
|---|---|---|
| Answer | Correctness, grounding, relevance | Reference answer and cited source |
| Tool use | Right tool, valid arguments, safe retries | Trace assertions |
| Policy | Permissions, refusal, approval | Expected control outcome |
| System | Latency, cost, availability | Operational thresholds |
6. Release with an owner and feedback loop
Use versioned prompts and tool schemas, a staged rollout, dashboards and rollback criteria. Review traces for recurring failure patterns and convert them into new evaluation cases. Production feedback improves the system only when it becomes structured evidence.
The strongest agent portfolio is not the one with the most tools. It is the one that can explain its boundaries, prove its behavior and fail safely.
Portfolio artifact
Document one agent with a one-page problem brief, workflow diagram, tool contracts, threat model, evaluation dataset, scored results, sample traces and release checklist. That package demonstrates more engineering judgment than an untested chatbot repository.
Primary sources
- OpenAI Agents SDK documentation
- Model Context Protocol architecture
- MCP security best practices
- NIST AI Resource Center
Written by Digital Edify curriculum team · Technical review required quarterly · Last reviewed 12 August 2026