Your agent demos beautifully and falls over in production.
The failure modes of distributed systems do not disappear because there is a model in the middle of them, and most teams shipping agents are discovering that the expensive way.
Agents fail in two expensive ways: the bill and the uptime. Both are infrastructure problems before they are AI problems.
Which is the job this practice has been doing for nine years: petascale operations, streaming services at scale, and the runbooks and alerting that keep them up.
What breaks
Three failures, over and over.
01
Cost
Tool definitions loaded on every call, context that grows without a ceiling, no per-agent budget. The bill triples and nobody can point at why.
02
Silence
No evals, so a wrong answer looks exactly like a right one. The system does not fail loudly. It fails quietly for six weeks.
03
Nobody on call
No tracing, no alerting, no runbook. When it goes down at 2am the founder is the on-call rotation.
Field note
I found this one in my own bill.
I built a multi-agent system on my own infrastructure. Roughly seventy-two skills were loading into context on every single call. Not when a skill got used. Every call. The definitions sat there whether the agent needed them or not.
My API bill blew up. I paid for it personally.
What I built in response is the same cost-governance layer I'd build for you. Per-agent budgets. Token instrumentation, so spend is attributable to a specific call and tool instead of a monthly total. Progressive tool disclosure, so definitions load on demand rather than all at once.
I'd rather have found it in my own bill than in yours. It's also why the audit starts by measuring instead of advising. I know how convincing a system looks right up until someone reads the invoice.
Seventy-two skills, loading on every call. The definitions sat there whether the agent needed them or not.
Who does the work
The engineer reading your code has run the infrastructure underneath it.
This practice came from the infrastructure side rather than the model side. That's the difference that shows up at 2am, when the problem isn't the prompt.
Nine years of continuous infrastructure practice, 2017 to now. Petascale operations: 1.405 petaFLOPS across 2,000+ servers and 8+ PB of storage, before building AI on that class of hardware. Streaming and event-driven services at scale, with the observability, alerting, runbooks and incident response that go with them.
So the review doesn't stop at the prompt layer. Retries, timeouts, queue depth, circuit breakers, where your secrets live, what your egress looks like, who can reach what. Those decide whether an agent survives contact with production, and they're ordinary infrastructure questions with well understood answers.
Security isn't a separate engagement. Building inside accredited environments means ATO artifacts, NIST SP 800-53 control mapping and assessor-ready evidence pipelines. The principal holds an active TS/SCI with polygraph and writes the code.
Almost no AI vendor has run the compute underneath the model.
Notes
Three things worth knowing before you buy anything.
Why agent costs scale wrong
Tool definitions sit in the context window on every call. Connect ten servers with twenty tools each and you're spending thousands of tokens before the user has said anything.
You pay it on every call, forever, whether or not a single one of those tools gets used. It's structural. A cheaper model doesn't fix it and neither does a patch. You fix it by controlling what's in the window.
Your demo isn't evidence
A demo shows the system can produce a right answer. It says nothing about how often it produces a wrong one, because without evals you can't tell the two apart.
So it doesn't fail loudly. It fails quietly for six weeks, and you hear about it from a customer instead of a dashboard. An eval harness is the difference between running a system and hoping.
MCP, and what it costs you
Model Context Protocol is a standard wrapper around APIs, so a model can use them without a programmer in between. It solves tool distribution, and it solves it well.
It doesn't solve retries, orchestration, evals or cost. It makes cost worse, because the menu lives in the context window. Every tool you connect gets paid for on every call, whether the model reaches for it or not.
Engagements
Four ways to start. One of them is free.
Discovery call
Free
20 minutes
What you're running and what breaks most often. No deck.
Working session
$500
90 minutes
One specific problem. Screen share, then a one-page written summary of what I'd do.
Agent Readiness Audit
$4,500
One week
Written findings: what breaks, what it costs, what is unmonitored. Prioritized fix list with effort and impact. 60-minute readout with the engineering team.
Larger engagements
From $22,000
Scoped
Production sprints and fractional retainers. Scoped to a named outcome.
Every engagement starts with the discovery call. Nothing above is billed until scope is named in writing.
The audit, day by day
What $4,500 actually buys.
A week is short enough to be worth risking and long enough to measure something real. Here's the whole week, so you're not buying a description.
Day 1
Read the code
Map the agent graph. What calls what, in what order, with what sitting in context. Find where the tokens go. Most of the surprises are visible by the end of the first day, and almost none are where the team expected.
Day 2
Instrument
Measure real cost per call, per tool and per agent. Not a monthly total. A number you can point at. You can't govern spend you can't attribute, and one figure on a provider dashboard isn't attribution.
Day 3
Failure modes
What happens on a retry, a timeout, a malformed output, a partial response. These are ordinary distributed-systems questions, and putting a model in the path doesn't answer any of them for you.
Day 4
Evals
Can you currently tell a good answer from a bad one? If not, that's the finding and everything else waits. Without evals you have no way to know whether a change helped.
Day 5
Written findings
Prioritized by effort against impact, so you can act on the top of the list without me. Including the things I think you should leave alone, and why.
Then
60-minute readout
With the engineering team, not just the person who signed. They'll ask the questions that decide whether any of it gets done.
That is the whole week. If it sounds like what you need, the next step is twenty minutes.
No deck, no obligation, and if the audit is not right for you I will say so on the call.
Work delivered by the principal in prior roles, shown here for scale. Not past performance of Agen2ic, LLC.
Questions
Things people ask before the first call.
We're pre-production. Is this too early?
No. It's the cheapest time. Cost control and evals are architectural, and retrofitting them into a system that's already carrying traffic costs a lot more than building them in now.
The audit is also shorter before production, because there's less code to read and fewer decisions to unwind.
We're on Bedrock, Vertex, or OpenAI directly. Does that matter?
Not much. The failures I'm looking for live in your orchestration, not your provider. What sits in context on every call. What happens on a retry. Whether you can tell a wrong answer from a right one. Those look the same whichever endpoint you're posting to.
Provider choice changes your unit economics and your compliance story. It doesn't change the shape of the problem.
Will you sign an NDA?
Yes. Send yours and I'll sign it before the discovery call if you'd rather talk specifics from the start. If you don't have one, I'll work to whatever your counsel prefers.
What if our problem isn't agents?
Then we find that out quickly, and it's probably still in scope. Agent failures are usually ordinary distributed-systems failures wearing a new hat: a retry storm, an unbounded queue, a timeout nobody set, a dependency with no circuit breaker.
That's nine years of infrastructure work, not a detour from it.
Can you work alongside our existing engineers?
That's the intent. The audit ends in a 60-minute readout with your engineering team, not a document dropped over a wall.
On larger engagements I work in your repo and your review process, and the goal is that your team can run what I leave behind without me.
What if the audit finds we're basically fine?
Then I tell you that, and you don't hire me for the sprint.
You get the written findings either way, including the parts that say this is fine, leave it alone. A report that finds a serious problem no matter what isn't a report. It's a sales document, and it's worth nothing to you by the third time you read one.
Federal capability
Cleared practitioner, not a cleared reseller.
We build and harden agentic AI inside accredited environments: Model Context Protocol deployment inside accredited boundaries, ATO artifacts, NIST SP 800-53 control mapping, assessor-ready evidence pipelines.
Principal holds an active TS/SCI with polygraph.
Personnel clearance held by the principal. Agen2ic, LLC does not hold a facility clearance; classified performance is executed under a sponsoring prime's FCL.