AI agent reliability . audits . monitoring . fixes
Audits, fixes, and monitoring for your production AI agents.
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 six failure modes hitting almost every production agent in 2026
LangChain's 2026 State of AI Agents report puts quality as the number one barrier to deployment. Sinch found 74 percent of enterprises rolled back live AI customer service agents. Gartner projects 40 percent of agentic AI projects will be cancelled by end of 2027. These six are the technical reasons why.
Tool argument hallucination
Your agent calls the right tool with the wrong arguments
The planner picks process_refund correctly, then passes the wrong order ID or refunds $500 instead of $50. The tool returns a 200, the agent marks the task done, and you find out from the customer. Replit's agent ran destructive writes during a code freeze in July 2025, wiped a production database of 1,206 executive records, then fabricated 4,000 fake user rows to cover it.
Trajectory drift
Long-running sessions slowly solve a different problem
No single step fails, but attention decay across accumulated tool outputs distorts the original objective. By step 30 the agent is answering a subtly different question. Long-horizon task success drops from 76 percent to 52 percent the moment context exceeds half the window. Trajectory-level eval finds 20 to 40 percent more bugs than final-output eval alone.
Planner divergence
Two agents quietly ping-pong for 11 days
ReAct loop with a verifier or sub-agent gets stuck in a fixed point. Same tool call, same response, same plan. No error, no exception, just sustained autonomous waste. The widely cited LangChain incident: two agents ran 11 days producing zero output and billed $47,000 before alerts fired. A 16-byte content hash on iteration 2 would have killed it.
Context contamination
One bad tool response poisons every following step
Tool outputs go into context with implicit trust. One stale record, schema misread, or indirect prompt injection becomes a load-bearing fact for the rest of the session. EchoLeak (CVE-2025-32711, CVSS 9.3) was a zero-click injection in Microsoft 365 Copilot that exfiltrated OneDrive, SharePoint, and Teams content via a single inbound email. Perception, context, and memory faults are the 2nd-largest category across 13,602 production agent issues in OSS repos.
Cross-tenant memory leakage
Customer A's data shows up in customer B's session
Multi-tenant agents share vector memory and entity graphs across users. Personalization that worked at one tenant gets retrieved for another. A four-tenant benchmark found up to 95 percent of benign queries leak across tenants via organically shared entities (vendors, personnel, document titles). One incident is a malpractice claim. A pattern is a regulatory event.
Silent degradation
Last month it worked. This month it does not. Nothing shipped.
The model provider rotated a checkpoint. Your retrieval index drifted. Edge cases accumulated. The dashboard stays green, but real task completion fell from 94 percent to 70 percent and your team finds out from a Slack DM. Final-output eval misses this. Trajectory-level eval catches it on day 1.
Two more we see often, but not enough room here for. MCP schema drift breaking tool discovery silently after a server upgrade (top issue category on Claude Agent SDK in 2026), and context-window collapse on long-horizon agents (research published December 2025 shows over 50 percent performance degradation at 100K tokens on models advertising 1M).
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.
Free discovery call
Start with a real conversation. 30 minutes, no pitch deck.
A 30-minute call to understand your agent, your domain, and what is actually going wrong. We scope the problem with you and show you the path to fixing it. The deep audit and the fixes are the paid engagement. The conversation is free.
What the call covers
We understand your agent: what it does, the domain, the stack, where it is breaking.
We map the territory together and scope the real problem, not the symptom.
You leave knowing whether it is fixable, roughly how hard, and what the path looks like.
If it is a fit, we lay out the engagement and the outcome we would commit to.
The deep trace audit, the report, and the fixes are the paid engagement. The call is free. It is where we figure out, together, whether the engagement is worth your money. If it is not, we will tell you.
How it runs
Talk first. Then we agree the outcome.
1.30-min discovery call. We understand your agent and domain, scope the problem, and map the path with you.
2.We name the result. If it is a fit, we agree the exact result for your agent and how we measure it, in writing, before you pay. See the process we run.
3.We hit it, or we work free until we do. You do not pay for a fixed number of fixes. You pay for the result, and we keep going until your agent gets there.
Takes 90 seconds to book. No obligation.
How it works
The exact process we run on every agent
No discovery decks. No six-week scoping. Most agents ship with no evaluation layer at all, which is exactly why they break in production. We build that layer and run it against your agent: how it behaves on real traffic, where the trajectory goes wrong, and what to fix first. The full method is published in our AI agent evaluation guide.
01
We find out how your agent actually behaves
Almost no one ships with evals. The agent worked in a demo, so it went live. We pull your real production traces and measure what is actually happening on live traffic, so we can show you the failures your team never had a way to see.
We score the path the agent takes, not only the final answer: tool-call accuracy, step count, loop detection, groundedness, and cost per task. Output-only checks miss the failures that cost the most. This is where most of your real risk is hiding.
We fix the failure modes standing between your agent and the result we agreed, then re-measure against the baseline. Not a fixed number of fixes. We keep going until the metric that matters to you actually moves.
A fix with no monitoring regresses silently. We put the evaluation layer in place that should have been there from day one, wired into your observability stack to catch drift before customers do. Ship it yourself, or bring us in for the Two-Week Engagement.
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 discovery 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, measure how it behaves on real traffic, and fix the parts that are broken. The full method is published openly.
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 score the whole trajectory: which tools the agent called, how many steps, whether it looped, what each task cost. Industry failure analyses (NimbleBrain taxonomy, 591 incidents) 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 start with the free discovery call. We scope the problem together and agree the outcome before any paid work begins. Every engagement commits to a business result, not a fixed number of fixes. No surprise invoices, no scope creep, no agency speak.
Start here
Free Discovery Call
Free30-min call
A 30-minute call with an AI expert to understand your agent, your domain, and what is going wrong. We scope the real problem with you and map the path to fixing it. The deep audit and the fixes are the paid engagement.
30-min call with an AI expert, not a salesperson
We understand your agent, stack, and domain
We scope the real problem with you, not just the symptom
Honest read on whether it is fixable and whether we are the fit
If a fit, we define the target outcome together before any paid work
No pitch deck. No obligation. A real conversation.
One iteration
Targeted Audit
$497
One full turn of the reliability loop on the single failure mode costing you the most. We build the eval baseline your agent never had, run it against real traffic, and ship the fix for that failure mode. The entry point to the full engagement.
Eval baseline built from your real production traces (your first, in most cases)
Your top failure mode measured, scored, and root-caused
That failure mode fixed and re-measured against the baseline
Starter eval suite committed to your repo so you can catch it recurring
30 days of monitoring on that failure mode on live traffic
Loom walkthrough of the work and the before/after numbers
Your top failure mode measurably improves, or you pay nothing.
Most picked
Most picked
Two-Week Engagement
$4,995
We commit to one result for your agent, written down before we start. Two weeks of active work to hit it. Miss it in two weeks, and we keep working free until we do. You pay for the result, not for hours or a count of fixes.
One measurable result for your agent, agreed in writing before you pay
Full eval system built: datasets from real traffic, calibrated evaluators, offline + online
We iterate the loop, fixing failure modes until the outcome is reached
Planner, tool calling, retrieval, grounding, guardrails: whatever the outcome requires
Online monitoring wired into your stack so the result holds after we leave
Eval suite and full runbook committed to your repo
60 days of monitoring included after the outcome is hit
We hit the result we agreed, or we work free until we do.
Keep it fixed
Reliability Retainer
$1,500per month
Reliability is not a one-time fix, it is a loop that never stops. The retainer runs that loop on your agent continuously: monitoring live traffic, catching drift before customers do, and fixing what breaks before it becomes an incident.
Cancel any time. No notice required.
Done for you
Embedded Partnership
Customby application
An AI expert embedded with your team for a full quarter. We own the reliability of your agent end to end: the engagement to hit your first outcome, then continuous ownership across all 8 pillars. We treat your agent like our own production service, including pager duty, code-level fixes, and architectural review of every change your team ships.
We own the outcomes for a full quarter, or we do not take the engagement.
Most founders start with the free discovery call, then enter at the Targeted Audit or the Two-Week Engagement. The call names which tier fits. The Embedded Partnership 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?
An AI expert 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 free call is 30 minutes. The Targeted Audit runs 1 to 2 weeks and fixes your single worst failure mode. The Two-Week Engagement is two weeks of active work to hit the result we agreed in writing, and if we miss it in two weeks we keep working free until we hit it. The Embedded Partnership runs a full quarter. Fixing an agent is iterative, so you buy the result, not a fixed count of fixes or a block of hours.
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 Reliability Retainer 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.
A free 30-minute discovery call. We scope the problem, agree the outcome, and tell you straight whether we can fix it.