AI agents that hold up in production

Q: What breaks in production agents?

Tool failures, loops, prompt drift, and silent wrong answers. We build guardrails for each: timeouts, retry budgets, human checkpoints, and logging you can audit.

Q: What is MCP?

Model Context Protocol: an open standard for plugging agents into tools. We build MCP integrations so your agents act inside your systems — creating tickets, querying databases, filing reports — not just chat.

Q: How do you test agents?

Scenario evals: scripted task suites run against every build, covering the failure modes we found in your assessment. Regressions surface before deploy, not after.

Scoping — no fixed tiers

Agent work varies too much for fixed tiers — we scope through the free assessment and quote fixed before work starts. No hourly meter, no open-ended engagement.

Step	What happens	Delivery	Price
Free assessment	We map the task, the tools the agent will touch, and the failure modes to guard against.	45-min call + 1-page roadmap in 2 days	$0
Fixed-scope build	Quoted from the assessment: MCP integrations, scenario eval suite, guardrails, human checkpoints, handoff docs.	Quoted	Typically $2K–$25K
Care plan	Monitoring, eval re-runs on new model versions, and guardrail tuning as your tools change.	Monthly	$99/mo

What's included

MCP integration with your tools — tickets, databases, calendars, APIs.
Scenario eval suite: scripted task tests run against every build.
Guardrails: timeouts, retry budgets, human-in-the-loop checkpoints, audit logging.
Handoff: runbook for your team covering the failure modes and how to respond.

Honest notes, before you spend: not every task needs an agent — many jobs are better served by a deterministic automation or a search interface, and the assessment will say so plainly. Agents also need your tools to have stable APIs; if the plumbing isn't there, we'll tell you what to fix first.

Care plan: $99/mo — monitoring, eval re-runs, and guardrail tuning. Defects against the agreed scope are covered by our 14-day defect window.

Before we start, we'll ask for

The job you want the agent to do — and what "done right" looks like.
Access to the tools and APIs the agent will act through.
Where a human must stay in the loop — we'll push for more, not fewer, checkpoints where the stakes are high.

Frequently asked questions

What breaks in production agents?

Tool failures, loops, prompt drift, and silent wrong answers. We build guardrails for each: timeouts, retry budgets, human checkpoints, and logging you can audit.

What is MCP?

Model Context Protocol: an open standard for plugging agents into tools. We build MCP integrations so your agents act inside your systems — creating tickets, querying databases, filing reports — not just chat.

How do you test agents?

Scenario evals: scripted task suites run against every build, covering the failure modes we found in your assessment. Regressions surface before deploy, not after.

Start the free assessment

AI agents that hold up in production.

Agents we've already built