Missed Google’s AI Agents Intensive? Here’s a field-tested playbook to ship reliable, safe, and cost-aware agentic apps—aligned with where Google and the industry are headed.
7 practical takeaways for production AI agents
- Define outcomes and SLAs up front: target success rate, tool-call accuracy, latency, and budget per session. Treat agents like products, not demos.
- Use tools and structured outputs: call APIs, databases, and internal systems with clear schemas. Favor JSON schemas and function-calling to reduce hallucinations.
- Ground responses: retrieve facts from your docs and data before the model answers. Retrieval-augmented generation (RAG) plus citations builds trust.
- Plan memory deliberately: keep short-term context in the session and long-term facts in a vetted store. Minimize PII retention and tokenize sensitive fields.
- Evaluate offline and continuously: build scenario test sets, run temperature sweeps, and measure task success—not just BLEU or ROUGE. Gate releases on evals.
- Add observability from day one: trace prompts, tool calls, model versions, costs, and errors. Tag sessions by cohort to catch regressions quickly.
- Design for safety and fallback: enforce policies, add input/output checks, require human approval for high-risk actions, and degrade gracefully to simpler flows.
Quick-start stack
- Model + API: start with Gemini via Google’s Gemini API or equivalent; prefer tool/function calling and JSON mode.
- Agent orchestration: align with Google’s direction in Vertex AI Agent Builder for production patterns and integrations.
- Retrieval: use a vector store (e.g., pgvector) plus a fast keyword index. Chunk smartly, store citations, and cache frequent answers.
- Evals: create task-focused benchmarks and track success, latency, and cost. Automate runs in CI before you ship.
- Safety: adopt a risk framework (see NIST AI RMF) and log policy violations for review.
One-page build checklist
- Write the agent’s contract: goals, constraints, tools, SLAs.
- Implement 2–3 core tools first; add more only if success stalls.
- Ground every answer; cite sources when possible.
- Ship with evals, tracing, and cost caps enabled.
- Route risky actions to human-in-the-loop and log outcomes.
- Review traces weekly; turn incidents into new test cases.
Key takeaway
Production agents win by reliability, not novelty. Instrument early, ground answers, and ship with evals and guardrails you can explain to stakeholders.
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