Every agent run should produce a trace: the input, system prompt version, each tool call with inputs/outputs, model responses, total latency, token count, and cost. Without traces, debugging agent failures is guesswork.

Tools like LangSmith, Helicone, Braintrust, or a custom OpenTelemetry pipeline work well. The key requirement is linking every step to a task ID so you can replay the exact sequence when a customer reports a problem. Aim to diagnose failures in under 5 minutes.

Build three views: a developer trace (full prompts and tool I/O for debugging), an ops dashboard (success rates, latency percentiles, cost trends by customer), and a customer-facing activity log ('Your agent checked 3 policy documents and found the relevant clause on page 12'). Each audience needs different detail levels.

Maintain a regression test suite of 20-50 critical workflows with expected outcomes. Run this suite on every prompt change, model swap, or tool modification. One bad deploy should never silently drop your resolution rate from 85% to 60%.

Build your golden dataset from production failures. Every time a customer reports a wrong answer or a missed step, add that scenario to the test suite with the correct expected behavior. Over months, this dataset becomes your most valuable engineering asset — it captures the exact edge cases your agent must handle.

Track pass rate over time and set a deployment gate: no prompt or model change ships if golden dataset pass rate drops below 95%. This single practice prevents more production incidents than any amount of manual testing.

Agents are black boxes by default, and black boxes make users anxious. Show users what the agent is doing in real time: 'Searching knowledge base for refund policy... Found 3 relevant articles... Checking customer eligibility... Drafting response.'

Transparency builds trust and reduces support tickets. When something goes wrong, a visible trace lets the user understand why ('Could not find a matching policy — escalating to human agent') instead of wondering if the system is broken.

Include citations and source links in every response. 'Based on Refund Policy v2.3, Section 4.1' is verifiable. 'Based on our policies' is not. Users who can verify agent outputs trust them enough to let them operate with less supervision over time.