Solutions
AI Agent Development Services
Production agent systems with tool routing, guardrails, evaluation harnesses and a cost model you can defend.
We build production AI agent systems using the OpenAI Agents SDK with all model traffic routed through LiteLLM. That means designed tool schemas, guardrails, trajectory evaluation, observability and explicit cost budgets — the engineering that separates a working demo from a system you can put in front of customers.
The problem this solves
Nearly every organisation we talk to has already built an agent prototype. It worked in a demo, then behaved unpredictably the moment real users arrived. The failure is almost never the model and almost never the prompt. It is that a prototype has no tool contract, no failure semantics, no way to detect a bad trajectory, and no budget ceiling.
Production agent engineering is systems engineering. Tools need strict schemas and idempotent behaviour so a retry cannot double-charge a customer. Failures need to be legible to the model so it can recover rather than loop. Every run needs a trace you can replay. And a change to a prompt needs to be gated by an evaluation suite, exactly like a change to code is gated by tests.
What we build
- Agent architecture using the OpenAI Agents SDK, with handoffs and explicit state
- Tool and function design: strict schemas, idempotency, informative failure messages
- Model Context Protocol (MCP) integrations for standardised tools and resources
- Evaluation harnesses that score whole trajectories, not just final outputs
- Guardrails: input validation, output constraints and human escalation paths
- Model routing through LiteLLM with failover, caching and per-feature budgets
- Observability: structured traces of every step, tool call, retry and token cost
How we approach it
Map the decision boundary
We start by writing down exactly which decisions the agent may make and which must stay with a human. Most agent failures trace back to this never having been decided explicitly.
Design tools before prompts
Tool schemas are the real interface. We design them for strictness and idempotency first, because a well-shaped tool eliminates whole categories of prompt problem.
Build the evaluation harness early
Before optimising anything, we build the suite that tells us whether a change helped. Without it, every subsequent decision is guesswork with a confident tone.
Instrument, then optimise
With traces and token accounting in place, cost and latency work becomes measurement-driven: route easy requests to cheaper models, cache what repeats, remove steps that add nothing.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot generates text. An agent decides what to do and then does it through tools — querying data, calling APIs, taking actions in other systems. That difference is why agents need guardrails, idempotency and evaluation, and chatbots largely do not.
Why route through LiteLLM instead of calling a provider directly?
A gateway gives you one place for failover, caching, cost attribution, rate limiting and tracing. Calling providers directly from application code spreads that logic everywhere and makes switching providers expensive. It is the same argument as putting a database behind a repository layer.
How do you control agent costs?
Through routing cheaper models to easier requests, caching what repeats, trimming context aggressively, reducing unnecessary reasoning steps, and enforcing per-feature budgets at the gateway. The achievable saving depends entirely on how the system was built, so we measure before promising anything.
How do you know an agent is working correctly?
With an evaluation harness that scores whole trajectories against a labelled set, run on every prompt or tool change. Manual spot-checking feels like verification but does not detect regressions, which is why systems that rely on it degrade quietly.
Can you work on an existing agent system?
Yes, and it is the more common engagement. We usually start by adding tracing and an evaluation harness, because you cannot improve a system whose behaviour you cannot measure.