Agents that own the workflow, not just the chat.
An AI agent development company builds systems that plan multi-step work, call real tools, and finish the job — or know exactly when to hand it to a human. That last clause is where the engineering lives, and it's what separates an agent in production from an agent in a demo.
BroutonLab is that kind of partner: a dedicated senior team that designs, builds, and operates production agents for founders building vertical AI products. ML in production since 2017, LLM products since 2023.
One intent. Many autonomous hands.
Customer-facing agents
Agents that resolve tickets, book appointments, and process requests end to end — grounded in your data, escalating to humans with full context when they should.
Back-office & ops agents
Invoice handling, data entry, report assembly, CRM hygiene — the repetitive multi-step work your team does between the work they were hired for.
Research & matching agents
Agents that search, rank, and shortlist across large datasets. Ours turns one English sentence into a search across millions of candidates — in production daily.
Voice agents — same brain, phone line
Agents on a real phone number: streaming speech, sub-second turn-taking, interruption handling, mid-call language switching, tool calls into live calendars. Ours makes thousands of calls a day for a US client.
Only need answers from your docs, not actions on your systems? That's a smaller build — see AI chatbot development.
Engineering that keeps agents honest.
Orchestration that survives step 7
Multi-step workflows fail in the middle, not the beginning. We design retries, checkpoints, and state so a failed step recovers instead of silently corrupting the run.
Human-in-the-loop by design
Every agent gets an escalation path and an approval boundary: what it may do alone, what it must ask about, and what it must never touch.
Evals before autonomy
An agent earns each permission by passing evals on your real cases. Accuracy, task completion rate, cost per run — measured before launch and monitored after.
Guardrails & audit trails
Bounded tool access, spending limits, full logs of every action and why. When an agent acts on your systems, you can always answer "what did it do and on whose authority".
Agents our clients run their businesses on.
AI sourcing agent
One English sentence becomes a search across millions of candidates — running daily inside a hiring platform used by recruiters.
Read the case →Voice agent at scale
Thousands of phone conversations a day for a US client: tool calling, language switching, calendar booking — same agent core, different channel.
Case study comingOne agent first. A workforce when it earns it.
Our AI agent development services start with a fixed-price pilot ($30–50K, 2–4 months): one agent, one workflow it fully owns, evals passed before launch. Scale from there with a dedicated team from $15K/month — as a custom AI agent development company we keep the same engineers on your product from pilot through rollout. Details — how we work; the engineering practice underneath — custom AI development. Want a number first? Try the AI agent cost calculator.
Questions founders actually ask.
How much do custom AI agents cost?
Our engagements start with a fixed-price pilot: $30–50K for 2–4 months — one agent owning one workflow, in production, with evals. Ongoing dedicated teams run $15–65K/month. The honest cost drivers: how many systems the agent must act on, how much can go wrong when it acts, and how much of the stack must run on your infrastructure.
Should we build agents in-house with frameworks instead?
Frameworks like LangGraph and CrewAI genuinely let your team build AI agent prototypes fast — and we use them ourselves where they fit. The gap is everything around the framework: evals, guardrails, recovery, cost control, and the integration work that makes an agent safe on your real systems. If you have senior engineers with bandwidth, build in-house; if you need it in production this quarter, that's what an AI agent development company is for.
What's the difference between an AI agent and a chatbot?
A chatbot answers; an agent acts. Agents plan multi-step work, call tools, write to your systems, and know when to stop and ask. The engineering bar is higher because the failure modes are real actions, not wrong answers — which is why we treat agents as production software, not prompts.
How do you keep an agent from doing something stupid?
By assuming it will try. Bounded tool access, approval gates on irreversible actions, spending limits, adversarial test cases in the eval suite, and monitoring that flags weird behavior before your customers do. No vendor can promise zero mistakes — we promise the blast radius is designed, not discovered.
How long until an agent is in production?
2–4 months for the first agent owning one real workflow — that's the pilot. A demo agent takes a weekend; the distance between demo and production is exactly where agent projects die, and it's the part we're built for.
Which stack do you use?
Model-agnostic by policy: commercial LLMs, open-weights models, or both — plus orchestration chosen per problem, not per partnership. For sensitive data the whole stack runs on your infrastructure. The vendor landscape shifts quarterly; your agent shouldn't depend on one bet.