description: Make the agent correct and trustworthy: typing, linting, testing, metrics, evaluations, guardrails, and security.

4. Quality

How will you make the agent trustworthy?

Make the agent correct and trustworthy with layers of evidence. Start with trusted types, warning-free checks, isolated state, and branch-covered tests. Add live model trajectory evaluation separately. Then enforce PII boundaries, human confirmation, transactional writes, deterministic adversarial regressions, and repository security scans.

This chapter covers:

• 4.0. Typing: Python typing with ty, parsing tool I/O at the boundary.
• 4.1. Linting: Lint and format with ruff and dprint.
• 4.2. Testing: Fast, offline unit tests with pytest, against an isolated dataset copy.
• 4.3. Metrics: A concrete scorecard of release gates and observed operational indicators.
• 4.4. Evaluations: ADK trajectories plus full-conversation MLflow lineage and optional judge evidence.
• 4.5. Guardrails: Boundary redaction, stable errors, confirmation, transactions, and audit evidence.
• 4.6. Security: Threat modeling, offline adversarial regressions, identity, and supply-chain scanning.

The chapter remains model-free until the evaluation page explicitly asks for a configured provider. A green interactive demo cannot substitute for these gates.