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AI Agents in Production: Lessons from the First Wave

AITriyash AI Practice April 22, 2026 9 min read

The gap between an impressive agent demo and a dependable production system is wide — but it's now well understood. Having shipped agents for support, operations, and data workflows, we see the same success factors repeat.

Narrow beats general

The agents delivering real ROI do one job: triage this queue, reconcile these invoices, draft this report. Ambitious 'do-everything' agents fail in ways nobody can debug. Scope is a feature.

Design the failure path first

Every production agent needs an explicit answer to: what happens when it's unsure? The good ones escalate to a human with full context. Confidence thresholds, human-in-the-loop review, and audit logs aren't overhead — they're what makes deployment possible.

Evaluation is the real engineering

Teams that succeed build evaluation sets before they build the agent: real inputs, expected outputs, measured continuously. Without it you're guessing whether v2 is better than v1.

The economics work

A support agent that resolves 40% of tickets autonomously, correctly, pays for itself in weeks. The technology is ready — the differentiator is engineering discipline.

We help teams go from agent idea to production deployment, including the evaluation and guardrail infrastructure that makes it safe. Ask us about our agent readiness assessment.

Let's build something worth talking about

Tell us about your goals. We'll bring the strategy, design, and engineering to make them real — starting with a free, no-pressure consultation.