AI Agents That Actually Work in Production

2025-01-28  ·  6 min read

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There's a graveyard of AI agent projects that worked beautifully in demos and fell apart the moment real users touched them. I've shipped a few of these myself. Here's what I've learned.

The Demo-Production Gap

In a demo, you control the inputs. You pick clean, well-formed requests that hit your happy path. In production, users will ask your agent to do things you never imagined, in sequences you never planned, with inputs that make no sense.

The first rule of production AI agents: assume chaos.

Design for Failure, Not Success

Every LLM call can fail, hallucinate, or return something structurally invalid. Your agent needs to:

  • Validate outputs before acting on them
  • Have fallback paths for every critical branch
  • Know when to hand off to a human

Don't build a beautiful happy path. Build a robust failure path and let the happy path take care of itself.

Structured Outputs Are Non-Negotiable

If your agent needs to make decisions based on LLM output, force structured output. JSON schema enforcement (available in the latest Claude and GPT models) is your friend. If the model can't return valid JSON matching your schema, that's a failure — treat it like one.

const result = await anthropic.messages.create({
  model: "claude-opus-4-6",
  max_tokens: 1024,
  system: "You are a support classifier. Respond only with valid JSON matching the provided schema.",
  messages: [{ role: "user", content: userMessage }],
})

Context Window Management

Agents that run long tasks accumulate context. Eventually they hit limits, start forgetting earlier steps, or start making decisions based on stale information.

Strategies that work:

  • Summarize completed steps rather than keeping full transcripts
  • Store critical facts in structured memory (a DB, not the context window)
  • Use checkpointing so a failed long-running task can resume

The Human-in-the-Loop Sweet Spot

Full autonomy sounds great. In practice, the most valuable agents are semi-autonomous — they do the tedious work and pause at decisions that matter.

For NexusWave's support automation, we found the sweet spot at about 80% autonomous. For the remaining 20% of tickets — edge cases, escalations, refund decisions — we surface a draft response to the human agent rather than sending automatically. This is what makes customers trust the product.