AI Agent Development — production agents that actually do work
Custom AI agents built on Claude, GPT, and open models — wired to your data, your tools, and your real workflows.
The gap between a chatbot demo and an AI agent that survives a production workload is enormous, and most teams fall into it. The demo answers questions about a PDF. The production agent has to authenticate, hit your real APIs, write to your real database, handle the case where the model hallucinates a customer ID, and not run up a £4,000 OpenAI bill on its first weekend. We build production AI agents. We start with the workflow you actually want automated — not the model — and work backwards: what's the data, what tools does the agent need to call, where are the safety rails, what does the operator see when something goes wrong. The result is an agent your team can hand a real ticket to, not a chat window in a Notion page. We work across Claude (our default for tool use and longer contexts), GPT-4, and open models like Llama 3 and Mistral when latency or cost rules out the frontier. If you want a tour, our own [/build/chat](/build/chat) consultation is a Claude-powered agent we shipped in production — it scopes projects, builds plans, and emails them as branded PDFs. Same patterns we'd use for yours.
About this service
What an "agent" actually means here
An LLM with tools, memory, and a clear job description
When we say "AI agent", we don't mean an autonomous, open-ended thinking machine. We mean an LLM scoped to a specific workflow, given a tightly defined set of tools (your APIs, your database queries, your email sender, your Stripe account), and supervised — either by a human who reviews actions before they execute, or by a guardrails layer that won't let the agent move money or write to production tables without explicit user confirmation.
That's the agent that actually ships. Anything more autonomous than that, in 2026, breaks in production. We'll tell you exactly where the supervised/autonomous line should sit for your use case.
Where we add the most value
Sales, support, ops, and internal-tool replacement
The agents that pay for themselves fastest are the ones that replace a specific repetitive workflow inside a company. Customer-support triage that drafts the reply and pulls the relevant order. Sales-ops agents that enrich leads from public data and write them into the CRM. Internal ops agents that turn a Slack message into a Linear ticket with the right tags. We've also shipped customer-facing agents — onboarding, scoping, ticket deflection — but the ROI on internal agents lands faster.
If you're not sure which workflow is the best first target, our scoping process starts with a 'workflow audit' — we look at what your team does manually and tell you which slices would actually benefit from an agent and which would just add latency.
Safety and observability
We treat hallucination as a system property, not a model property
Models hallucinate. That's not solvable at the model layer in any general way. What we do solve, at the system layer, is the cost of a hallucination — by constraining tool inputs, validating tool outputs, logging every decision the agent made, and making it cheap to roll back or audit anything the agent did.
Every agent we ship has a full audit log of inputs, model responses, tool calls, and tool results. Your ops team can replay any conversation. If you ever need to explain to a customer (or a regulator) why the agent did what it did, you'll have the evidence.
Adjacent work
Often paired with Automation Scripts and API Integration
An agent is only as good as the tools you give it. Most engagements include a layer of automation scripts (the deterministic plumbing the agent calls into) and API integrations (the third-party services the agent reads from and writes to). If you also want the agent reachable from messaging surfaces, see Telegram bot development, WhatsApp Business API, or Discord bot development.
Real ai agent development patterns we’ve shipped
Not adjectives. Specific shapes of build we’ve taken to production for clients like you.
Project-scoping agent (like ours)
A Claude-powered consultant that interviews a buyer, builds a written project plan, and emails it as a branded PDF — exactly what powers [/build/chat](/build/chat) on this site.
Customer-support triage agent
Reads incoming tickets, pulls the relevant customer record and order history, drafts a reply for human review, and tags the ticket — saves L1 support 60–80% of triage time.
Sales-ops lead enrichment
Agent watches your CRM for new leads, enriches them from public sources, scores them against your ICP, and writes the result back into the lead record.
Internal docs Q&A on top of real data
Retrieval-augmented assistant on your company wiki, Slack history, and product docs — with citations, scoped to per-employee permissions.
Code-review and PR-summary agent
Reads GitHub PRs, generates plain-English summaries for non-technical stakeholders, flags risky diffs against your team's review checklist.
Voice-to-CRM agent
Reps drop a 30-second voice note after a call; the agent transcribes, extracts the next steps and the action items, and writes them straight into the CRM.
Refund / cancellation deflection
Customer-facing agent that handles refund and cancellation requests inside policy bounds, escalates the edge cases, logs everything for audit.
How a AI Agent Development engagement actually runs
Five concrete steps with deliverables. No retainer fog.
Workflow audit
We sit with the team currently doing the work manually and document the steps, the data they touch, and the edge cases. You leave with a written workflow map and an estimate of how much of it an agent can actually own (usually 60–85%).
Tool & data design
We define the agent's tools (your APIs, your queries, your senders) with strict JSON schemas, the data the agent reads from (vector store or direct DB queries), and the supervision policy — what's auto, what's confirm-first.
Prompt + eval harness
We build the system prompt iteratively against a real evaluation set drawn from your historical workflow data. Every change to the prompt gets scored before it ships. No vibes-based prompt engineering.
Production wiring + observability
Audit log, conversation replay, cost dashboard, latency p95/p99, hallucination flags, and a kill switch. The agent goes behind your existing auth, talks to your existing services, and shows up in your existing observability stack.
Pilot, tune, scale
Two-week supervised pilot with one team, weekly tuning based on real conversation logs, then a graduated rollout. We don't sign off until the agent's quality metrics hold steady at full traffic.
Real brackets, no surprise invoices
Starting points. Exact quote on the scoping call — written, fixed, no hourly surprises.
Agent Pilot
One workflow, one team, 3–4 weeks
- Workflow audit + eval set
- Single-tool or single-channel agent
- Audit log + cost dashboard
- 2 weeks supervised pilot
Production Agent
Multi-tool, multi-channel, 6–10 weeks
- Multiple tools + RAG over your data
- Web + Slack + email channels
- Confirmation policies + kill switch
- Full observability dashboard
- 60 days of tuning support
Agent Platform
Multiple agents, shared infra, retainer
- Reusable agent platform on your infra
- New agents added monthly
- Continuous eval + cost tuning
- Model upgrades managed for you
Things real buyers ask before paying
If yours isn’t here, ask on the scoping call.
Often shipped alongside this
Automation Scripts
Custom scripts and workflow automations that quietly do hours of work in the background, every day.
Telegram Bot Development
Telegram bots that handle real users, real payments, and real moderation — not toy bots.
API Integration Services
Bidirectional, idempotent integrations between your CRM, billing, comms, and product — built to survive retries, schema drift, and outages.
Real builds in production
Ready to scope a AI Agent Development build?
60-second AI consult and you’ll leave with a written plan. Prefer humans? Drop a custom quote request — we reply within a working day.