Theory and frameworks are useful. But the real lessons come from shipping. Here are patterns we’ve seen across multiple agent-augmented product initiatives—what worked, what didn’t, and what we’d do differently.

Case 1: Internal DevTool with Agent-Assisted Code Review

What we built: A tool that runs agent-generated code review comments before human reviewers see the PR. The agent suggests improvements; the human approves or edits.

What worked: Review time dropped 40%. Reviewers reported higher confidence—they caught more issues. The agent’s suggestions became a checklist; humans didn’t have to hold the full rubric in their head.

What didn’t: Early versions were noisy. The agent flagged too much—style opinions, nitpicks—and reviewers tuned out. We had to train the prompts and add filters: “only surface high-impact comments.”

Takeaway: Agent output needs calibration. More isn’t better; relevance is. Invest in prompt design and output filtering.

Case 2: Product Spec Generation for a Discovery Team

What we built: Agents that draft product specs from research findings and stakeholder input. PMs refine and approve.

What worked: Specs went from days to hours. PMs could iterate faster—try multiple directions, compare options. The agent’s first draft was often 70% right; the PM’s job shifted to validation and strategy.

What didn’t: Specs were sometimes generic. “Build a dashboard” produced boilerplate. We learned to inject more context—user personas, constraints, success criteria—and to use examples from past good specs.

Takeaway: Context is everything. Generic prompts produce generic output. Invest in rich, structured context.

Case 3: Multi-Agent Pipeline for Feature Development

What we built: Research agent → Design agent → Code agent → Review agent. Each handoff was structured; humans intervened at checkpoints.

What worked: End-to-end cycle time for small features dropped dramatically. We could go from “idea” to “code in PR” in a day. The pipeline forced discipline: clear handoffs, clear ownership.

What didn’t: Coordination overhead was real. Debugging “which agent failed” was hard. We added tracing and logging. We also found that some handoffs needed human synthesis—the agents couldn’t always translate perfectly between domains.

Takeaway: Multi-agent systems need observability and explicit handoff contracts. And some handoffs will always need a human in the middle.

Common Themes

  • Calibrate output. Filter, rank, or constrain. Raw agent output is often too much or too generic.
  • Context matters. The more you give, the better the output. Invest in context management.
  • Design for failure. Agents will get things wrong. Build validation, escalation, and fallbacks.
  • Observability is non-negotiable. When things break, you need to trace the chain.

Applied AI works when we treat it as engineering—with the same discipline we apply to any complex system.