AI agent development services
Agents that plan, act, notice when they were wrong, and know when to stop and ask.
An AI agent decides what to do next and then does it - calling your APIs, reading what came back, and adjusting. We build them with the parts that decide whether one survives contact with production: a narrow capability boundary, confidence thresholds, an approval step before anything irreversible, full action logging, and an evaluation harness that tests the decisions rather than the prose.
From $4,500-8,000 for a single-purpose agent, $8,000-18,000 for a production agent and $14,000-26,000 for an agent platform. Our own agentic build repairs 87% of failures on the first loop and cut debugging from 4-5 hours to under 15 minutes.
Who this is for
Teams with a workflow that is mostly rules and occasionally judgement - the kind that resisted automation because the exceptions were too varied to enumerate. Triage, reconciliation, research, first-line support, code maintenance. If a competent new starter could do it with access to your systems and a week of training, an agent can usually do a large share of it.
What you get
- 01An agent loop with an explicit, narrow set of capabilities - the specific operations it needs, scoped to the acting user's permissions.
- 02Confidence thresholds and an escalation path, so low-confidence cases reach a person instead of being guessed at.
- 03An approval step in front of anything irreversible, and a dry-run mode for everything else.
- 04Full action logging: what it decided, why, what it called and what came back - reproducible after the fact.
- 05An evaluation harness that scores tool selection and outcomes, not just final text, and reports the spread across repeated runs.
- 06A kill switch: a feature flag that stops the agent per account without a deploy.
You do not need an engineering team to start
Describe the job you want done in plain words - answering customer questions, sorting incoming requests, pulling figures out of documents. On the first call we tell you whether AI is the right tool for it, roughly what it would cost and how long it would take.
If it is not worth building, we say so. If you are building a whole product rather than one tool, read how an AI product build works.
- Single-purpose agent$4,500-8,000
An assistant that answers your customers' questions from your own help pages and product documents, with a link to where each answer came from.
Typically 2-4 weeks - Production agent$8,000-18,000
An assistant that reads each incoming request, looks the customer up in your systems, drafts the reply and waits for a person on your team to approve it before anything is sent.
Typically 4-7 weeks
How we build it
- 01week 1
Capability boundary
Before any code: what the agent may do, what it must never do, and what needs a human. Written down and agreed. This conversation is uncomfortable and it is the one that prevents the expensive incident.
- 02week 1
Evaluation set
Real cases from your ticket history or logs, labelled with the correct outcome and, where it matters, the correct action. Including the awkward ones - an eval set of easy cases proves nothing.
- 03week 2
Tools first, loop second
Each capability built and tested as an ordinary function with its own tests, before the agent is allowed to call it. Most agent bugs are tool bugs wearing a costume.
- 04weeks 3-4
The loop
Planning, tool calls, result reading, retry and self-correction, with a hard step budget so a confused agent stops rather than spiralling.
- 05week 5
Thresholds and escalation
Confidence scoring, the handoff to a human, and the interface that person sees - which needs the agent's reasoning, not just its conclusion.
- 06weeks 6-7
Shadow run
The agent runs alongside your existing process without acting, and you compare. This is how an agent earns permission to act, and it is where most of the real tuning happens.
Where agents actually fail
| Failure | What it looks like | What we build |
|---|---|---|
| Confidently wrong | A plausible answer with no basis, delivered without hedging | Grounding in retrieval plus a confidence threshold and an explicit refusal path |
| Spiralling | Twelve tool calls to answer a question that needed one | A hard step budget, and a loop detector that stops on repeated identical calls |
| Silent capability creep | A tool added for one purpose being used for another | Narrow capabilities, scoped per user, with an audit log reviewed in the first weeks |
| Regression after a prompt tweak | A fix for one case quietly breaking three others | A regression suite of previously-fixed failures, run on every change |
| Cost blowout | A loop that is cheap in testing and expensive at real volume | Cost per interaction instrumented from week one, with alerts on the per-user figure |
The self-healing engineering agent is this service taken to its limit: an agent that generates code, tests it in an isolated Docker sandbox, reads the failure, retrieves the relevant context and patches only the faulty block.
The retrieval layer underneath most useful agents is described on the RAG systems page.
- Single-purpose agent$4,500-8,000One job over your own content, one or two integrations. 2-4 weeks.
- Production agent$8,000-18,000Multi-step, a few integrations, human handoff and approval paths. 4-7 weeks.
- Agent platform$14,000-26,000Several agents or systems, shared tooling, permissions and evaluation. 10-16 weeks.
The full breakdown of what drives the number, with a worked example, is in what AI agents actually cost to build. Agencies reselling this should start at white-label AI agents.
Frequently asked questions
An agent decides what to do next and then does it - calling tools, reading results, and changing its plan based on what came back. A chatbot answers. The engineering difference is everything that surrounds that loop: what it is allowed to do, how it knows it failed, and what happens when it is confident and wrong.
We price agents in three tiers. Single-purpose agent (one job over your own content, one or two integrations): $4,500-8,000. Production agent (multi-step, a few integrations, human handoff and approval paths): $8,000-18,000. Agent platform (several agents or systems, shared tooling, permissions and evaluation): $14,000-26,000. The driver is integration count and how wrong it is allowed to be - the gap between an agent that drafts and one that sends is often 2x.
Capabilities are explicit and narrow: the agent gets the specific operations it needs and nothing else, scoped to the acting user's permissions. Anything irreversible goes behind a confidence threshold and, usually, a human approval step. And every action is logged with the reasoning that led to it, so a bad decision can be reproduced rather than argued about.
Run each case several times and report the spread rather than the best result. Test the tool calls the agent chose, not just the final text. And keep a regression set of the failures you have already fixed - in agent work, the same failure comes back after an unrelated prompt change more often than anywhere else in software.
Plain application code by default. LangGraph or Temporal when a workflow genuinely needs durable state across hours or days. Most agent frameworks add indirection that makes debugging harder, and debugging is where the time actually goes.
2-4 weeks for a single-purpose agent; 4-7 weeks for a production agent; 10-16 weeks for an agent platform. The first week is spent on the evaluation set and the capability boundary, before any agent loop is written.
Start with the shadow run
Tell us the workflow. We will run the agent alongside your current process so you can compare before committing.