AI agent development for UK SaaS, fintech, and B2B teams. We build agentic AI on LangGraph and MCP that takes real actions on real systems: writes to your DB, posts to Slack, schedules jobs in HubSpot, sends invoices in Xero. State-machine architecture, eval gold sets, audit log on every action, human escalation where it matters. The kind of agent your team can defend in a Series A diligence call.
A weekend agent is a beautiful demo that becomes a customer complaint in 72 hours. We build the state machine, the eval set, and the audit log that make it production-grade.
The agent was clever. It used GPT-5. It had tool access to her Gmail, HubSpot, and Notion. It was supposed to draft outreach emails for the sales team. On Wednesday it drafted an email, attached the wrong customer’s NDA, and sent it without review.
The architecture wasn’t the model’s fault. The agent had no state machine. No human-in-the-loop. No audit log of which tool it had called with which args. It was a beautiful demo that became a real customer complaint inside 72 hours.
This page is for founders who want agents that survive past the demo. The next sections show the state-machine discipline that makes them safe.
Raw LangChain lets agents do whatever the model decides next. LangGraph forces every step through a named node with a defined exit. That’s the difference between a research notebook and a production system.
Every state is a function. Reading the graph tells you what happens at each step.
Only valid transitions allowed. No “model decided to call function X that doesn’t exist”.
Agent can pause for human input + resume hours later. State survives a server restart.
Every run is a chain of nodes with inputs and outputs. Replay any case in a sandbox.
The discipline that keeps an agent defensible in a Series A diligence call, and an FCA audit.
Every transition typed, every node named. No swarm chaos. Auditable in production.
Actor + tenant + tool + args + result + timestamp. Exportable for any compliance review.
Confidence threshold + escalation. Refund over £X? A human approves. Always.
100-300 tasks, CI-gated. A regression over 5% blocks the deploy. 94% median pass at launch.
Each one is the difference between an agent you can defend and one you roll back on Wednesday.
“Our agent sent the wrong file to a customer. We had to apologise.”
Human-in-the-loop for irreversible actions. Any send, charge, delete, or commit-to-customer action goes through a human approval gate. Drafts queue in a dashboard with the agent’s reasoning.
“The agent kept calling tools in a circle and burned £2K in an hour.”
Step budget + cycle detection. Hard step cap per run. Cycle detection breaks loops. Token budget per run. Over budget escalate, not retry. Zero infinite-loop incidents in 18 months.
“The agent made a weird call and we can’t figure out why.”
Full trace per run, replay in sandbox. Every node logs input, output, tool calls. Replay any production run in a sandbox. Tweak the prompt, the tool, the routing.
“Beautiful demo on one customer. Falls over with ten.”
Production-shape architecture from week one. Multi-tenant from day one. State persisted in MongoDB. Queues. Per-tenant rate limits. The demo IS the production system.
“Every new tool requires a custom integration. We’ve written eight already.”
MCP servers + tool registry. Tools defined once via Model Context Protocol. Any agent (or any model) can call any tool. 12 MCP servers built for clients in 2025.
“How do we prove what the agent did when an auditor asks?”
Append-only audit log on every action. Every tool call: actor, tenant, tool, args, result, timestamp. Exportable. Searchable. Read-only. Your auditor reads the log and asks no more questions.
“We changed a prompt and the eval dropped 12 points. Now we’re scared to deploy.”
Eval-gated CI + auto-block on regression. Gold set runs on every PR. Drop more than 5% and the deploy is blocked. Quality stops being scary.
“OpenAI deprecated a model. Now our agent needs a full rewrite.”
Model-agnostic graph + gateway. The graph runs on any model. Swap Claude to GPT-5 to Llama in config. Re-run the gold set. Cut over. Done four times in 2025.
The agent patterns we know cold.
KYC triage, sanctions screening, anti-fraud, FCA-aware design.
Onboarding, churn signals, internal helpdesk automation.
Contract triage, clause extraction, redline drafting.
Symptom intake, urgency triage, clinician hand-off.
Lead enrichment, outreach drafting, CRM updates.
Letting inbox triage, maintenance booking, document chase.
LangGraph for the state machine, MCP for the tools, and the AWS layer that runs the agent fleet.
A Series A fintech onboards 4,000 merchants/month: 82% auto-approval, 0 sanctions breaches, FCA audit passed first time. What clients say after six months.
82% auto-approval on KYC and zero sanctions breaches. The FCA auditor called it the cleanest agent trail he'd seen.
Pick the shape that fits and Mohit will send your real number inside 24 hours.
5 days. We map your agent ambition, surface what’s automatable, write the graph.
6 to 10 weeks. One LangGraph agent end-to-end: graph, tools, evals, audit log, dashboard.
7 to 10 weeks. Take your existing chain, rebuild as a state graph, cut over safely.
“82% auto-approval on KYC and zero sanctions breaches. The FCA auditor called it the cleanest agent trail he’d seen.”
— Henry G., CTO, UK FinTechPain-first, soft-second.
LangGraph is graph-structured: every state transition is explicit. Swarm frameworks let agents cycle without your knowledge. That’s fine in research, harder to defend in production. If you have a strong reason to prefer another framework, we’ll work in it, as long as the eval + observability discipline stays the same.
Every action that’s irreversible (send, charge, delete, commit-to-customer) goes through a human approval gate. Drafts queue in a reviewer dashboard. The reviewer sees the agent’s reasoning, approves, edits, or rejects. Agents do reversible work autonomously, escalate the rest.
Every run is traced. You replay the failure in a sandbox. Tweak the prompt, the tool, the routing. Add the case to the gold set. CI catches it forever. Bugs you fix once stay fixed.
Eval gold set of 100-300 real tasks with known correct answers. Built with your team in week one. Every PR runs against it. Pass rate is a number you both watch. Below threshold = deploy blocked.
The graph is model-agnostic. Swap Claude to GPT-5 to Llama in config. Re-run the gold set. Cut over. Done four times in 2025 for clients without customer impact.
30-day walk-away both ways. Milestone billing 25/25/25/25. UK VAT registered, listed on Companies House, shipping since 2019.
Every AI agent development project has two senior engineers paired. Every decision into an ADR same day. Mohit reviews every PR. Two handovers in seven years, both inside 48 hours.
Yes, with 14 days’ notice. Engineers move to other projects. Spend pauses. Resume with 14 days’ notice. No cancellation fee.
ai agents, in context — the dashboards, flows and components your team actually ships, reviews and maintains.
Tell us the action the agent should take, the tools it needs, and what your team needs to defend. Mohit replies inside 24 hours: a clear yes, a clear no, or the one question that decides it.
Yes, no, or the deciding question. Straight to your inbox.
We map the state machine and build the eval set with your team.
Typed graph, audit log, human gates. Clean enough for an FCA review.