The prototype impressed everyone. Now ship it
You built an agent that demos perfectly. Deploying it is where things fall apart. Memory, authorisation, scaling, monitoring, compliance — we build that layer, on whatever framework your agent already uses.
37
agents we run in production across four systems
3
clouds in production, each with infrastructure as code
~3 wks
to a first working system, and we say when it will not be
Why agent prototypes stall on the way to production
Four things go wrong, and they go wrong in the same order every time. None of them are model problems.
The prototype works, production fails
The proof of concept impressed the room, and then scaling it broke everything that was hand-waved to get the demo working.
Framework lock-in
The orchestration library's assumptions become your constraints, and customising past them costs more than the library saved.
No memory persistence
Agents forget context between sessions, so the second conversation starts from nothing and users stop trusting it.
Missing observability
Nobody can trace why an agent decided what it decided, so a production issue becomes archaeology rather than debugging.
The layer between a demo and a system
Everything an agent needs to be run by people who were not in the room when it was written.
Framework agnostic
Deploy any framework — LangGraph, CrewAI, Google ADK, AWS Bedrock AgentCore, or a custom orchestration where a framework would fight the problem.
Production infrastructure
Memory, authorisation and monitoring built in from day one, rather than discovered as missing during the security review.
Built to scale
Queue-driven auto-scaling, state checkpointing, circuit breakers and two-tier retry, so a slow dependency degrades the system instead of stopping it.
Full observability
Trace every decision, monitor cost per run, and debug a specific failed request rather than guessing from an aggregate.
Building the plumbing, or building the product
The infrastructure under an agent is well-understood work, and it is still months of it. The question is whether your senior engineers should be the ones doing it.
Building it in-house
- Four to six months before the first production request
- Five to eight engineers on plumbing rather than product
- Memory, auth, tracing and scaling each solved from scratch
- Ongoing maintenance owned by the team that built it
Deploying with us
- Weeks rather than months to the first production request
- One or two engineers to run it, and they keep the source code
- The infrastructure patterns already running in our own systems
- Guardrails written in code, not in prompts, and testable as code
What an agent tier is worth, in your numbers
We do not publish a savings figure, because ours would be a guess about your operation. Here is the arithmetic instead. Fill in your own values and the answer is yours rather than ours.
- The fully loaded cost of one handled request today, including the person handling it.
- Monthly volume — how many of those requests actually arrive.
- Deflection rate — the share an agent can close without a person, which starts lower than anyone hopes.
- The run cost of the agent tier itself: model calls, infrastructure, and the engineer who watches it.
Bring those four numbers to the call and we will run the arithmetic with you. If it does not clear, we will say so on the first call rather than the third.
Bring the agent that will not ship
This is our AI engineering practice
It is real work and it is where our four products came from. But what Cognilium leads with is narrower: optimization apps that run in tandem with Microsoft Dynamics 365, computing the decisions the ERP records but does not derive — the optimal price, the optimal pick path, the optimal stock level. See the optimization apps · How we build inside the ERP.