Agentic AI in Production
Agents that survive production traffic — and don't quietly break at 3am.
Most AI agents break in production. They hallucinate, loop, or fail silently. We build agentic systems with the guardrails, fallbacks, and observability to stay working under real load.
Who Agentic AI in Production Is For
- Teams whose first agent demo worked but won't survive production traffic.
- Operations leaders looking to automate multi-step internal workflows without a brittle script tax.
- Founders evaluating whether agents are the right shape for their problem at all.
How Agentic AI in Production Works
- Step 01
Scope and contain
Map the workflow end-to-end, draw a hard boundary around what the agent owns, and define the failure paths before any code is written.
- Step 02
Build with guardrails first
Tool-use schemas, structured outputs, retries, timeouts, and human-in-the-loop checkpoints, shipped before the happy-path demo.
- Step 03
Instrument before launch
Traces on every step, evals on the orchestration policy, cost ceilings per run, and alerts that fire on loop or stall conditions.
- Step 04
Operate and tune
Weekly review of failure cases, regression evals on policy changes, and cost-per-task reduction as the workflow stabilizes.
What you get
- A production-deployed agent with documented tool contracts and guardrails.
- Observability stack: traces, evals, cost dashboards, alerting.
- Runbook covering common failure modes, retry policy, and human escalation.
Where we've shipped this
All case studies →Frequently asked questions
How is agentic automation different from regular workflow automation?
Regular automation follows fixed steps. Agentic automation makes decisions: which tool to call, when to ask for human input, when to give up. That flexibility is also where it breaks. We add the guardrails, fallbacks, and observability that keep an agent's decisions honest under real load, with hard limits on cost and iteration count.
What does production-grade really mean for an agent?
It means the agent has a runbook for the on-call engineer at 3am, evals that catch behavioral regressions before users do, fallbacks for when tool calls fail, observability for every decision the agent makes, and cost limits so a runaway loop does not invoice you for thousands of tokens. Demo-grade has none of these.
When is an agent the wrong shape for a problem?
When the workflow is fully deterministic, an agent adds latency, cost, and failure surface for no upside. Use a script. When the decisions need real human judgment with high stakes, an agent should be assisting a human, not replacing one. We turn down agent work where a simpler tool would do the job better.
How do you keep an agent from looping or burning tokens?
Hard limits on iteration count, token budget, and tool-call depth, enforced before the agent starts. Observability that flags when the agent is approaching a limit. Fallbacks that escalate to a human or a deterministic path when the agent gets stuck. Evals that catch loop-prone prompts before they reach production.
Let's build something that actually works.
Tell us where you are and what you need. We'll come back with a clear, honest plan within 48 hours.