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
Related: automation and AI readiness, AI security and governance, the KPATH platform.