Every AI agent development company will show you an impressive demo. The demo is the easy 1%. What decides whether the agent survives contact with production is the unglamorous 99%: the eval suite that proves this week's agent is better than last week's, the approval queue in front of every write action, the audit trail your compliance team can read, the token metering that keeps the CFO off your back, and the kill switch for the day something drifts. That layer is what we build first — it's also most of what the canceled 40% skipped.
Our build process is the five survival rules we wrote about in why AI agent projects fail, applied in order: start from one workflow the business already measures; put deterministic rules first and treat the LLM as an untrusted component; human-in-the-loop wherever the agent writes; budget build and run from day one; scope compliance before the pilot. None of it is glamorous. All of it is why our agents are still running a year later.
Regulated industries are where agent projects most often die at the last mile — legal blocks the rollout because nobody designed for the regulatory surface. We build agents for healthcare and fintech on a compliance-first architecture: PHI isolation and masking before the model sees data, BAA-covered infrastructure end to end, immutable audit logs of every tool call, and approval gates on any action that touches a patient record or moves money. The full reference architecture is public in our HIPAA-compliant AI agents guide.
We open-sourced Kite, an agent framework built on one principle: the LLM is an untrusted component. Kernel-level validation of every action, circuit breakers, idempotency, kill switches, and five reasoning patterns (ReAct, ReWOO, Tree-of-Thoughts, Plan–Execute, Reflective). You get the same architecture our production builds use — inspectable on GitHub, not a black box you rent.
A scoped workflow agent typically lands at $15K–$75K; compliance-bound and multi-agent systems run $70K–$500K depending on surface. The number most quotes hide is the running cost — token economics, monitoring, model churn — and we budget it with you from week one, with routing that sends easy steps to cheap models. Full numbers by agent type in our AI agent cost guide. We price by phase, from $10K/month, fixed per phase, and you own the IP, source code and repository from day one.
The most valuable thing an AI agent development company can tell you is when you don't need one. If the workflow has fixed inputs and fixed rules, a hundred lines of boring code beats an agent on cost, latency and reliability — and we'll say so in the first call. Broader AI work (generative features, RAG, ML pipelines) lives with our AI development services; this page is for when the agent is the point.
It designs, builds and operates AI agents that act — call tools, update systems, handle workflows — rather than just answer questions. The real work is the governance around the model: scoped tools, eval suites, approval gates, audit trails, cost metering. The model is a component; the engineering around it is the product.
In a 30-minute call we'll map the workflow, tell you honestly whether it should even be an agent, and give you a fixed price per phase — including the running costs most quotes hide.