AI DEVELOPMENT
We build autonomous AI agents that own one job end to end - reading the inputs, deciding, acting in your systems, and escalating what they should not decide alone. Scoped, governed and monitored, with a human able to take the controls at any point.
What AI agent development actually means
An AI agent is a scoped autonomous worker: it owns a defined job, uses tools and data to do it, and escalates what it cannot finish. AI agent development is the work of turning that description into something that survives production - the job definition, the tool interfaces, the memory, the failure paths and the limits.
The hard part is not making an agent work once. It is making it work on the ninety-third input of the day, the one with a missing field and an attachment nobody expected. That is why most of what we build is not the happy path. It is the retry logic, the confidence threshold, the escalation route and the log that lets you reconstruct what happened.
We build agents singly and in groups. One agent handling one narrow task is a real project and often the right first one. When a process needs several, they get an orchestrator, and that is a different discipline with its own page.
You probably need an agent if you recognize these
- A person opens the same queue every morning and works down it the same way.
- The work is judgment-light but context-heavy - anyone could do it, but only after reading four systems.
- You have automated one step of it, and a human still stitches the rest together.
- Volume rises and the only lever you have is another hire.
- The work happens on your team’s clock, but the demand does not.
What you get
The agent itself
A scoped worker with a written job definition, running on the cheapest model that clears its quality bar.
Tool and system access
Governed, per-agent credentials for the systems it needs, and nothing else.
Memory and context
What it remembers between runs, what it looks up fresh, and what it is never allowed to cache.
The escalation path
The confidence threshold, the queue it escalates into, and the context package it attaches when it does.
Evals and monitoring
A test suite that runs against real inputs, plus live sampling and alerts when quality drifts.
The runbook
How to pause it, correct it, retrain it and hand it to somebody else.
How an agent handles a job
AI agent development services
Trigger
A new item arrives - a ticket, a row, a file, a webhook. The agent wakes for that item only.
Self-healed - retried with fallback tool. Human not required.
Gather
It reads what it needs from your systems, with credentials scoped to this job.
Decide
It forms an action and a confidence score against the rules agreed in the blueprint.
Act
Inside its autonomy boundary, it acts. Outside it, it prepares the action and waits.
Log
Every input, tool call, model call and output is recorded before the item closes.
Escalate
Low confidence, an unknown case or an irreversible step routes to a person with the context attached.
Agents we have built this way
Ticket Triage Agent
Reads every incoming ticket, tags it, finds the answer and drafts the reply.
Invoice Matching Agent
Matches invoices to purchase orders and receipts, and stops the ones that do not reconcile.
SEO Content Agent
Researches, briefs and drafts, then routes to a human editor before anything publishes.
Where your agents will work
If it has an API, an agent can use it. If it does not, we build the bridge.
- Helpdesk and ticketing
- CRM
- ERP and finance
- Code repositories
- Data warehouse
- Email and calendar
- Chat
- Storage and document systems
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
What the agent may and may not do
Autonomy boundary
Written before the build, enforced in code. The agent’s permissions are the boundary, not a policy document about the boundary.
Approval gates
Anything irreversible - money out, a message to a customer, a deletion - waits for a named approver.
What stays human
Judgment calls, exceptions outside the defined set, and any decision the agent scores below its confidence threshold.
Logging
Every action, input, tool call and model call is recorded, so any decision can be reconstructed after the fact.
How the work runs
Timings are typical ranges from our engagement model (doc 04 §5), not a quote. The audit is what turns them into your dates.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | Readiness audit scores your candidate processes. One agent is prototyped on your real data and measured for accuracy, latency and cost per task. |
| Build | 3-8 weeks | Production build: integrations, retries and fallbacks, observability, secrets handling, deployment. Evals and approval gates are built in parallel. |
| Release | 1-2 weeks | Shadow mode, then assisted, then autonomous, once the pass rate holds. |
| Managed | ongoing, optional | We watch evals, route to cheaper models that still clear the bar, repair drift and add agents. Or we hand over with the runbook. |
What we measure
- <30 days
- Audit to first agent in production (Typical)
- 24/7
- Coverage, from the day it goes autonomous (Target)
- 100%
- Of agent actions logged and reconstructable (Target)
- Your baseline
- Cost per task, measured before and after (Yours)
“ Until then: these are design targets and typical ranges, not results. Your numbers come out of the readiness audit and the model on [the ROI calculator](/roi-calculator/).
What this looks like in practice
Support triage hive
Reference scenario · SaaS. Three channels, one queue, and a first response in minutes rather than hours.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
How is an AI agent different from an automation script?
A script follows a fixed path and breaks when the input changes. An agent reads the input, decides what to do against a goal, and can take a different route to the same outcome. The practical difference shows up on messy inputs: the script fails and stops, the agent either handles the variation or escalates it with an explanation. Agents also cost more to run, which is why we script the parts that genuinely are fixed.
How long before an agent is doing real work?
Typically under 30 days from the readiness audit to an agent running in production on a narrow task, with the first version often running in shadow mode before the full build finishes. Broad, cross-system processes take longer, because the integration work dominates. We tell you which one you have during the audit, before you commit to a build.
What happens when the agent gets it wrong?
It is caught, logged, and either retried or escalated. Every agent has a confidence threshold below which it stops rather than guesses, and an approval gate on anything irreversible. Repeated failures raise an alert with the full trace attached, so the fix is a change to the eval suite and the agent, not an argument about what happened.
Do we need to change our systems first?
Usually not. Agents work through the APIs your systems already expose, with credentials scoped to the one job they do. Where a system has no usable API, we build the bridge, and that effort is estimated in the audit rather than discovered halfway through the build.
Can we run this ourselves afterward?
Yes. We build to hand over: the runbook, the eval suite, the monitoring and training for your team are deliverables, not upsells. Many clients still choose a managed AI workforce because keeping evals honest is ongoing work, but that is a decision you make after the build, not before.
What does it cost to run, not just to build?
Cost per task, not per seat. An agent's running cost is model calls plus compute, and it is measured against the loaded cost of the human hour it replaces. We publish the shape of this on the pricing page and model your figure with you rather than quoting an average.
Related services
Custom AI & Machine Learning Development
Models trained on your data for problems a general-purpose model handles badly.
Generative AI Development
Products and features built on language, image and voice models.
Agent Orchestration
The layer that makes many agents behave like one team.