AI agent reliability . audits . monitoring . fixes
AI agents that work in production.
You paid someone to build your AI agent. Now it hallucinates to customers, invents policies you do not run, and burns through your monthly budget on retries you cannot see. We audit, fix, and monitor production AI agents so yours stops embarrassing you and starts doing the job you bought it for.
The four things every business hears the week before they call us
Sinch reports 74 percent of enterprises rolled back live AI customer service agents in 2026. Gartner predicts 40 percent of agentic AI projects will be cancelled by end of 2027. The pattern is consistent across every business we see.
Your AI bill is going up. Your results are not.
The agent retries failed steps over and over without telling anyone. Your monthly token bill doubles. You find out from the invoice, not from anything inside the product.
It is making things up to your customers
It invents prices, promises returns you do not offer, books appointments on days you are closed. Each one is a refund, a chargeback, or a bad review. You only find out after the customer screenshots it.
It used to work. Now it does not. Nothing changed.
Last month it answered correctly 9 times out of 10. This month it is 6 out of 10 and nobody touched the code. Your AI is silently getting worse and the dashboards do not show it.
Your developer says it tests fine
Internal tests pass. Real customers break it constantly. Your developer cannot reproduce the failures. You are stuck between a happy demo and an unhappy customer base.
What we work on
If it is an AI agent in production, we have probably seen it break
Industry does not matter. What the agent does does. We work with businesses running every kind of AI agent, across every vertical. The patterns we find are remarkably similar.
Customer facing
The agent that talks to your customers
Support, intake, lead qualification, scheduling. The agent your customers see directly. When this one misbehaves, you lose deals, lose trust, and end up on screenshots.
Internal ops
The agent that runs your back office
Document review, research, data entry, reporting, internal workflows. The agent your team relies on to save hours. When this one drifts, your team quietly goes back to doing the work by hand.
Autonomous
The agent that runs on its own
The agent that plans steps, calls tools, and finishes a job without asking. Built on Claude Agent SDK, OpenAI Agents SDK, LangGraph, or custom code. When this one loops, you find out from the invoice. One documented case ran 11 days and billed $47,000 before anyone noticed.
Real estate, e-commerce, fintech, legal, healthcare, logistics, agencies, services. The industry changes, the way agents break does not. If you have an AI agent in production, we can audit it.
The Agent Audit
$2,600 of value for $50
This is not a discovery call where we ask questions and book a follow up. We do the work. You get a real deliverable. The $50 is a commitment device, not a price. If it is not useful, you get every dollar back and you keep everything.
Inside your $50 audit
15 minute Loom walkthrough of where your agent is breaking
$500
Written report with the top 3 fixes, ranked by impact, with diffs for your developer
$600
Our full 60 point AI Agent Reliability Checklist (PDF and Notion)
$300
Starter test suite your developer can run on every change
$700
30 day private Slack channel for follow up questions
$500
Total stack value
$2,600
Your price today
$50
fully refundable
Risk reversal
Not useful. Full refund. You keep everything.
We charge $50 at booking so the only people who show up are founders who actually want their agent fixed. After the audit, if you do not have at least one usable fix you can ship, send a one line email and we refund every dollar. You still keep the Loom, the report, the checklist, the eval template, and the Slack access.
We have not had to refund one yet. The offer still stands.
Takes 90 seconds to book. We do the rest.
How it works
From booking to a hardened agent in two weeks
No discovery decks. No 6 week scoping. We look at your agent, run the benchmarks, and tell you what is wrong.
01
You book in 90 seconds
Pick a slot. Answer 4 qualifying questions about your agent stack and failure modes so we walk in prepared. No back and forth emails.
02
We check 60 things in 48 hours
We run your agent against our 60 point reliability checklist, look at how it actually behaves on real traffic, and find where it is wasting your money.
03
You get a Loom and a written report
A 15 minute walkthrough of every issue we found, plus a written report with the 3 fixes that will move the needle most. Each fix comes with the exact change your developer can make.
04
You decide what is next
Ship the fixes yourself, hand them to your engineer, or bring us in for the Reliability Sprint. No pressure, no follow up emails.
Composite engagements
What the work actually looks like
Three composite scenarios drawn from recent engagements. Client names redacted under NDA, technical specifics are real, headline metrics are directional. We will walk through the actual architecture, eval setup, and code-level work on the kickoff call.
Legal-tech startup · LangGraph agent pipeline
The problem
Their staged agent pipeline researched case law and drafted client memos. The agents would occasionally cite cases that did not exist and once surfaced an opinion from a different jurisdiction without flagging it. The founder had paused the rollout to paying firms until they could prove the output was verifiable.
What we did
Replaced the freeform retrieval step with a deterministic verifier that grounds every citation against their licensed case-law source before the drafting agent can use it. Added a per-jurisdiction filter at the retrieval boundary. Wrote a regression eval suite (180 historical memos) that runs on every prompt change. Built a per-matter trace viewer the founder can show prospects on demo calls.
Fabricated or wrong-jurisdiction citations
4 to 6 percent of outputs0 across 600 generated memos4 weeks
Business impactFounder unblocked the launch. Closed two paying law firms in the following month, the agent was the demo.
B2B content company · CrewAI multi-agent article pipeline
The problem
Their article-generation crew (planner, researcher, writer, editor) had been working for months, then started looping. Two agents would reflect back and forth on the same draft for 40+ turns before timing out. Token spend tripled. About one in five articles never finished.
What we did
Audited the crew. Found a reflection-loop trigger in the editor agent's prompt that fired on a class of edits it could not actually make. Rewrote the editor with explicit exit conditions and a hard step cap. Added per-task cost budgets that abort gracefully. Instrumented the crew with structured traces so the team can see exactly where each article spent its tokens.
Article completion rate
78 percent finished99.4 percent finished3 weeks
Business impactToken bill cut by 61 percent the following month. The team stopped manually rerunning failed jobs.
Their in-app agent answered product questions for paying customers using RAG over their documentation and product DB. Eval scores looked fine. Real users kept getting wrong answers about pricing tiers and feature limits. Support tickets were doubling, not halving, after the agent shipped.
What we did
Pulled six weeks of real production traces. Found the retrieval was returning the right docs but the agent was confidently extrapolating beyond them. Rewrote the system prompt with hard scope limits and a refusal trajectory. Connected the agent to the live pricing and entitlements service via a tool call so it could not invent tier rules. Built an online eval that scores every live answer against the ground-truth tier table.
Wrong-answer tickets
47 per week3 per week5 weeks
Business impactSupport deflection went from negative to plus 38 percent. The CTO presented the eval setup to their board.
These are composite scenarios. Each one is grounded in a real engagement, but specifics have been anonymized and numbers have been rounded to protect client confidentiality. Architecture, eval setup, and the engineering work we describe are accurate. Happy to walk through the real underlying engagements under NDA on a call.
Why founders pick us
You do not need an AI strategy. You need your AI agent to stop misbehaving.
There is a real difference between AI consulting and what we do. Most consultancies run workshops and write decks. We get inside the actual AI agent and fix the parts that are broken.
We only do agent reliability
Not a general AI agency. Not a dev shop. Not LLM strategy. We benchmark, evaluate, harden, and monitor production AI agents. That is the entire business.
We measure how, not just what
Most teams only check if the final answer looks right. We check how the AI got there. Industry failure analyses (NimbleBrain agent failure taxonomy, 591 incidents reviewed) attribute 88 percent of production agent failures to infrastructure and trajectory issues, not the model itself.
You do not need to write code
We hand findings to your engineer with exact diffs against your repo, or we implement them ourselves. You stay in the founder seat, not the on-call seat.
The savings usually pay for the work
We typically cut your AI bill 40 to 60 percent in the first two weeks, because half of it was being wasted on failed retries. Most clients tell us the savings alone covered our fee inside 60 days.
Pricing
A clear ladder. Start where you are.
Most founders enter at the $50 Audit, decide if we are a fit, and pick the right next step from there. No surprise invoices, no scope creep, no agency speak.
Start here
Agent Audit
$50fully refundable
The fastest way to find out what is silently breaking in your agent. Loom walkthrough of your traces, written report with 3 prioritized fixes, plus our full reliability toolkit.
15 min Loom walkthrough of your traces
Written report with 3 prioritized fixes
60 point Reliability Checklist (PDF + Notion)
Starter trajectory eval suite
30 day Slack channel for follow up
Not useful? Full refund and you keep everything.
Productized
Diagnostic Pack
$497
Everything in the Audit, plus we install your eval suite, ship 1 priority fix ourselves, and monitor for 30 days. The fastest paid path to a more reliable agent.
Everything in the Agent Audit
We install the eval suite in your stack
1 high impact fix shipped by our team
30 days of trace and trajectory monitoring
Weekly regression report
Pass rate baseline plus 15 percent or full refund.
Most picked
Most picked
Reliability Sprint
$4,995
2 week hands on engagement. We fix the top 3 failure modes from the audit, harden the planner and tool layer, and rebuild your trajectory evals. Most founders book this after the audit.
Audit, install, and fix top 3 failure modes
Planner and tool schema hardening
Full trajectory eval suite built around your stack
Written runbook for your team
60 days of monitoring included
Eval pass rate plus 30 percent or we keep working free.
After remediation
Production Confidence
$1,500per month
Continuous trajectory monitoring, weekly regression evals, monthly reliability report, and a direct channel when something breaks at 2am.
Cancel any time. No notice required.
Done for you
Reliability Reset
Customby quote
Full reliability ownership. We audit, sprint, and run monitoring for a quarter. Named retainer, weekly reviews, on call for incidents. For founders who want this off their plate completely.
We work until your eval pass rate hits 95 percent.
Most founders start with the $50 Audit. Around 40 percent move to the Diagnostic Pack or Sprint within 14 days. The Reliability Reset is for teams who want this off their plate completely, that one is by application.
FAQ
What serious founders ask before hiring us
Which frameworks and stacks do you actually know?
Production work on Claude (Anthropic SDK, Claude Agent SDK, MCP servers), OpenAI (Assistants API, Agents SDK, Responses API), LangGraph, LangChain, CrewAI, AutoGen, Mastra, LlamaIndex, Pydantic AI, and Vercel AI SDK. Plus the surrounding infrastructure most agents need (Pinecone, Weaviate, pgvector, Postgres, Redis, Temporal, Inngest). If you tell us your stack, we will tell you honestly whether we are a fit before you pay anything.
Who actually does the work?
A senior engineer with hands on production agent experience, not an account manager handing off to juniors. Every audit, sprint, and remediation is done by the same person you talk to on the discovery call. That is also why we cap the number of active engagements per quarter.
How long does a real engagement actually take?
The Audit is 48 to 72 hours after kickoff. The Diagnostic Pack is 1 to 2 weeks. The Reliability Sprint is 2 weeks scoped, with a clear deliverable on day 14. The Reset is 12 weeks. We do not run open-ended engagements, every project has a fixed scope, a fixed end date, and a written runbook on the way out.
What if our agent is more or less complex than your usual work?
We will tell you straight on the first call. If your agent is a simple single-step Claude wrapper, you probably do not need us, and we will tell you that and refer you to a freelancer who can fix it for less. If your agent is a multi-team production system with custom infrastructure, we will scope a Reset and bring in the right specialist for the parts outside our core (security review, infra scaling).
How do you handle IP and confidentiality?
Mutual NDA before any technical conversation, signed within an hour of your request. All code stays in your repository, all credentials stay in your secret manager, we never copy production data off your systems. We have worked under SOC 2, HIPAA, and bar-association ethical wall requirements. References from prior engagements available under NDA.
Can we bring this in-house after working with you?
That is usually the goal. Every engagement delivers a written runbook, your eval suite committed to your repo, your traces flowing into your observability stack, and a Loom-based handover to your team. We are not interested in turning ourselves into a permanent dependency, the Production Confidence tier is opt-in, not required.
Ready when you are
Stop holding your breath every time a customer talks to your AI. Let us look at it.
$50 to book, refundable in full if it is not useful. A 15 minute Loom from a real engineer. A written report your developer can act on tomorrow.