AI Agent Development
Outcome, method, and proof
Three ways to evaluate whether this is the right service line for your team.
Agents that act, not just chat
Your agent reads systems, calls tools, makes decisions, recovers from failures, and ships output your team can act on. It survives the failure modes most pilots ignore — partial tool failures, ambiguous inputs, edge cases the training data never saw.
Reliability as design constraint
We architect tool use, memory, and decision loops with the production failure modes named upfront. Eval criteria, observability, and recovery paths exist before the agent ships — not bolted on after the first production incident.
Brunel
Our flagship product is a planning agent in production today, used to coordinate AI coding agents on real engineering work. 82% of agent failures trace to poor pre-execution planning; Brunel exists because we lived inside that statistic.
Three steps to a production agent
Most agent development engagements run 6–14 weeks from kickoff to production deployment.
Discovery
What tasks, what tools, what failure modes? We map the agent’s job, the systems it’ll touch, the recovery paths when things go wrong, and the eval criteria that say “good enough to ship.”
Build
Agent architecture, tool definitions, memory model, decision loop. Eval harness running from day one. Observability built in. Production rollout plan defined before the agent ever runs against real users.
Deploy & tune
Phased rollout with monitoring at each phase. Eval-driven tuning during the first month of production. Optional ongoing retainer for capability expansion and model updates as the ecosystem evolves.
Where this fits — and where it doesn’t
Self-qualify before the call.
Best fits
High signal · book the call- Teams ready to build agents that act, not just chat
- CTOs concerned about agent reliability in production
- Companies with structured workflows agents can automate
- Engineering teams comfortable with eval-driven development
- Operations leaders looking to scale capacity without scaling headcount
Not a fit
Low signal · we'll redirect- Chatbot-only requirements (use a simpler tool — Intercom Fin, Zendesk AI)
- Greenfield “what should our agent do” exploration (define use case first)
- Projects with no eval criteria (we won’t ship without them)
- Workflows where any single error is catastrophic (human-only is the right answer)
- Demo-quality work with no production deployment plan
Questions buyers actually ask
Q.01 How is this different from a custom chatbot? +
A chatbot answers. An agent decides, calls tools, and takes action. A chatbot’s failure mode is “wrong answer.” An agent’s failure mode is “wrong action with real-world consequences.” That difference is what makes agent development a different discipline — and why the eval criteria matter more.
Q.02 Which model providers do you work with? +
Claude is our primary, by depth of expertise. We also build with OpenAI, Gemini, and open models when the use case calls for it (latency, cost, on-prem requirements). Discovery scopes the model decision; we don’t have a religious commitment to any one provider.
Q.03 How do you handle agent failures in production? +
Recovery paths are designed before the agent ships. Every external call has a defined timeout and fallback; every decision has a confidence threshold and a human escalation path; every tool has a “this isn’t working, stop trying” exit condition. Plus full observability — when an agent fails, we know why within minutes.
Q.04 Can you build on top of Brunel? +
Yes. Brunel is built for AI development planning specifically; if your agent project lives in that domain, building on Brunel can save weeks of architecture work. If it doesn’t, we build from scratch using the same patterns Brunel taught us.
Talk to a senior agent engineer.
30 minutes. We’ll walk through the agent you’re trying to build, the failure modes you’re worried about, and whether building on Brunel makes sense.