AI agents that can reason, plan, and execute multi-step tasks are no longer research curiosities — they're production systems handling real workloads. But the gap between a demo agent and a production agent is enormous.

After deploying dozens of agent systems for Australian businesses, here are the lessons that matter most.

Reliability Is Everything

A demo agent that works 80% of the time is impressive. A production agent that fails 20% of the time is unusable. The difference between the two comes down to:

Structured outputs — Force your agent to respond in predictable formats. Use tool schemas, JSON mode, and validation layers to catch malformed responses before they propagate.

Graceful degradation — When an agent can't complete a task, it should fail informatively rather than silently or catastrophically. Design fallback paths for every critical workflow.

Retry logic with backoff — Transient failures from API rate limits, network issues, or model timeouts are inevitable. Build retry mechanisms that don't amplify problems.

Human-in-the-Loop Design

The best production agents know when to ask for help. Design clear escalation paths:

• **Confidence thresholds** — If the agent isn't confident in its decision, route to a human reviewer.
• **High-stakes actions** — Anything irreversible (sending emails, modifying data, making purchases) should require explicit approval until trust is established.
• **Audit trails** — Log every decision, every tool call, every piece of context the agent used. You'll need this for debugging and compliance.

Monitoring and Observability

You can't improve what you can't measure. Instrument your agents with:

• Task completion rates and latency
• Token usage and cost per task
• Error categorisation (model errors vs tool errors vs logic errors)
• User satisfaction signals

Start Simple, Iterate Fast

The most successful agent deployments we've seen start with a narrow, well-defined task — processing invoices, triaging support tickets, generating reports — and expand scope only after proving reliability at each stage.