Building AI Agents That Survive Production

AI agent demos look magical. Production agents fail in boring ways: bad tool calls, infinite loops, silent retries, and missing audit trails. Shipping AI agents that handle real customer workflows means treating them like distributed systems with evals, policy, and observability.

Related: AI automation that actually ships and Auroviq solutions.

Production requirements for agents

1. Evals on real traces—not only golden prompts in a notebook
2. Human-in-the-loop for irreversible actions (refunds, deletes, outbound email)
3. Structured logs for every tool call, model version, latency, and cost
4. Timeouts, retries, and circuit breakers
5. Least-privilege tools—agents should never hold god-mode credentials
6. Replayability for incident review

Reference architecture

Planner → tool router → policy checks → execution → memory/audit. Keep the LLM swappable. Ground answers in retrieved company data when accuracy matters. Version prompts and tools like any other production artifact.

Common failure modes

• Prompt injection via user content or retrieved documents
• Tool loops that burn budget overnight
• Hallucinated arguments to APIs
• No kill switch when quality collapses
• Missing evaluation before expanding autonomy

Safe rollout plan

Shadow mode → limited beta with approvals → expand autonomy only where evals pass. Measure task success rate, human override rate, and cost per successful task—not vanity chat volume.

Frequently asked questions

Should every workflow be agentic?

No. Start with high-volume, medium-risk workflows where tools are well-defined. Avoid fully autonomous money movement early.

Which model should we pick?

Pick for latency, cost, and eval score on your data. Keep an abstraction layer so you can switch providers.

How does Auroviq build agents?

We ship agentic workflows for CRM, support, and ops with production rigor—evals, policy gates, and observability included.