The demo worked. Production is a different problem.
We help engineering teams get from an agent that impresses in a meeting to one that is reliable enough to depend on and cheap enough to keep running.
Agents that are convincing at ten requests and unusable at ten thousand.
The gap between a working prototype and a production agent is wider than most teams expect, and it is rarely about the model. Accuracy drops as the tool surface grows. Cost per request climbs in ways nobody forecast. Failures are hard to reproduce because the same input does not always produce the same path.
By the time these show up, a team has usually committed to an architecture. Reworking it is expensive, so the agent either ships in a diminished form or stalls in a pilot that quietly gets defunded.
Architecture that survives contact with real traffic.
Which of these matters depends on how far along you are. Teams with nothing built need different help from teams with six agents and a cost problem.
Readiness and cost assessment
An honest read on what your current agents cost per useful outcome, where that cost is going, and which parts of the workload are a poor fit for an agent at all.
Agent architecture and role design
How work is split between agents, how much each one should be trusted to decide, and where a deterministic step is a better answer than a clever one. Most reliability problems trace back to this.
Tool surface design
Accuracy falls as the number of tools in front of a model grows. Narrowing what any single agent sees at any moment is usually the single most effective change available, and it lowers cost at the same time.
Framework selection
Which framework fits the work you have, what each one commits you to, and where mixing them is reasonable. We have no allegiance to any of them, which makes this a shorter conversation than it usually is.
Reliability and evaluation
How you know an agent still works after a change. Evaluation sets, regression testing and the operational habits that let a team ship without holding its breath.
Operational cost control
Where the spend is going, which requests are worth what they cost, and how to attribute cost to the team or workflow that caused it rather than to a single opaque line item.
Assess, design, then help you build it.
We can stop after the design if your team wants to take it from there. Plenty do, and that is a reasonable outcome.
Assess what exists
Reviewing the agents you have running or prototyped, what they cost, where they fail, and what the team has already learned. The failures are more informative than the successes.
Design the target architecture
Roles, boundaries, tool surfaces, evaluation approach and cost model, sized against the traffic you actually expect rather than a demo. Delivered as something a team can build against.
Support the build
Working alongside your engineers through the first production deployment, or reviewing at intervals if you would rather own the delivery. We adapt to how your team prefers to work.
A design your engineers can build from on Monday.
- An assessment of current agent cost, accuracy and failure modes
- A target architecture with agent roles and boundaries defined
- A tool surface design, including what each agent should not see
- A framework recommendation with the commitments it carries
- An evaluation approach so changes can be shipped with confidence
- A cost model tied to expected traffic rather than demo volume
Questions we get asked about this work.
We have already picked a framework. Is this still useful?
Usually yes, because most production problems are architectural rather than framework-specific. Role design, tool surface and evaluation matter regardless of what you built on, and we would rather improve what you have than argue for a rewrite.
Our agents work fine but cost too much. Where do you start?
Almost always with the tool surface. Handing a model far more capability than the request needs inflates every call, and narrowing it tends to improve accuracy and cost together. After that it is a question of which requests justify their cost at all.
Do you write code, or only advise?
Both, depending on what is useful. Some engagements end at a design your team implements. Others involve working alongside your engineers through the first production deployment. We would rather be told which you want at the start.
How does this relate to KPATH AMP?
They solve different problems. This work is about building agents that are reliable and affordable. The platform is about governing what agents are permitted to do once they exist. Teams often need one long before the other, and which comes first depends on the size of the estate.
Related: automation and AI readiness, AI security and governance, the KPATH AMP platform.
Get the architecture right before the traffic arrives.
A free 30 minute conversation about what you have built and where it is straining.