Your team built a LangChain agent in a notebook. It worked. The demo was beautiful. Then you shipped it. Three weeks in, customers reported answers that didn’t make sense and your team couldn’t reproduce them. The chain ran 12 tool calls when it should have run two. Token bills came in at 4× the estimate.
Service LangChain development · LangGraph + LangSmith · UK
LangChain development that ships to prod. not a research notebook.
LangGraph engineering for UK SaaS, fintech, and B2B platforms. Notebooks look great; production is another job — graph-structured flow, deterministic exits, eval gold sets, and a runtime you can defend at 3am. 14 LangGraph systems shipped in 18 months on MERN + Python + AWS.
24hreply, from a senior
200+projects shipped since 2019
Senioronly, on the spine
(Why platforms sign)
The LangGraph runtime your CTO can defend. Fourteen agentic systems live. Zero loops, zero silent regressions.
LangChain gets you to a demo fast. We bring the LangGraph discipline that gets you to something a customer can rely on and an auditor can read.
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The story every AI lead knowsdemo beautiful · 3 weeks in · 4× the token bill
The notebook agent was beautiful. Prod ran 12 calls, not two.
The pass rate, if you were lucky enough to measure it, dropped 18% with no obvious cause. The library wasn’t the problem. The discipline around it was. Notebook LangChain and production LangGraph are different jobs. One is research, the other is engineering.
We do the engineering. We rebuild it as a LangGraph state machine: named nodes, typed edges, a hard step cap, LangSmith tracing every run. This page is for the team whose notebook works and whose production doesn’t.
A chain is a notebook. A graph is a system.
Both use LangChain primitives. Only one is safe to put in front of customers.
The notebookfast to demo
What it is
- Model decides the next step, freely
- No enforced state transitions
- Loops possible, no cycle detection
- Hard to trace what happened
- Great for prototyping + research
The systemsafe in prod
What we build
- Named nodes, typed edges
- Only valid transitions allowed
- Step budget + cycle detection
- Every run traced in LangSmith
- Persisted state, resumable, replayable
What we build with from the LangChain ecosystem
Six components, one system
The LangChain stack, used the production way. Graph for control, LangSmith for sight, evals for proof, MCP for tools.
Control
LangGraph state machine
Named nodes, typed edges, step budget. The control layer that keeps it safe.
Sight
LangSmith observability
Trace every run, every node, every cost. Replay any production case.
Proof
Eval gold sets
100-300 tasks, CI-gated. Quality becomes a number you both watch.
Tools
MCP tool servers
Tools defined once, reused across graphs. Provider-agnostic.
Retrieval
RAG pipelines
Vector store, re-ranking, freshness windows. Citation-grounded.
Routing
Multi-model gateway
Per-node model routing. Swap providers in config. Budget caps.
The pain on the left. Day-1 LangGraph architecture on the right.
Each one is the difference between a notebook that demos and a system that ships.
- 01
“The notebook worked. Three weeks into production, customers get wrong answers.”
State-machine graph + eval gold set from week one. LangGraph from day one. Gold set of 100-300 real tasks built in week 1, CI-gated. Production accuracy is the launch criterion, not demo accuracy.
- 02
“The agent ran 12 tool calls when it should have run two and burned the tokens.”
Typed edges + step budget + cycle detection. Only valid transitions allowed. Hard step cap per run. Cycle detection breaks loops. Zero infinite-loop incidents in 18 months.
- 03
“We changed a prompt and quality dropped 18%. We can’t tell which prompt did it.”
Eval gold set + CI gating. 100-300 representative tasks with known answers, run on every PR. CI blocks deploys regressing more than 5%. Quality stops being ‘feels right’ and starts being a number.
- 04
“A customer says the agent made a weird call and we can’t reproduce it.”
LangSmith trace + replay tool. Every run is a chain of nodes with inputs and outputs. Replay any production case in a sandbox. Tweak the prompt. Compare before and after.
- 05
“LangChain shipped a breaking change and we rewrote half the code.”
Pinned versions + tested upgrade path. We pin LangChain versions. Upgrade path tested against your gold set before deploying. Five major-version migrations done in 18 months without breaking client production.
- 06
“Our agent only works on GPT-4 and switching costs are huge.”
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.
- 07
“The agent took an action a human should have approved and the customer complained.”
Persisted state + human-approval node. LangGraph can pause for human input and resume hours later. Irreversible actions queue in a reviewer dashboard. State survives a server restart.
- 08
“We have a LangChain notebook in production. We need LangGraph. Now what?”
Audit + gold set + side-by-side rebuild. Take the existing chain. Re-model as state nodes with typed edges. Verify pass-rate parity. Roll out at 5% / 25% / 100% behind a flag. Done four times.
Recent client · UK fintech · 2024
Merchant onboarding agent.
Merchant onboarding agent.
4,000 a month, 82% auto-approve
A Series A fintech needed to onboard 4,000 merchants a month with KYC and AML checks. Their first attempt was a LangChain notebook that escalated everything to humans. We built a LangGraph agent that ingests documents, runs against policy rules plus Companies House and sanctions lists, drafts a recommendation with reasoning attached, and escalates only the unclear cases.
Safety
0
Sanctions breaches
82%
KYC auto-approval
Quality
94%
Eval pass at launch
1st
FCA audit, passed first time
Track record
14
LangGraph systems since 2024
5
Version migrations, no breakage
WHY TEAMS SIGN
Four things notebook teams and offshore shops can’t hand you
The discipline that turns a clever chain into a production system an auditor can read.
LangGraph state machines
Every transition typed. Every node named. No swarm chaos, auditable in production.
Eval discipline before code
100-300 gold-set tasks built in week one. CI-gated quality from day one.
LangSmith observability
Every node logged. Every tool call traced. Replay any production run in a sandbox.
Step + token budgets
Hard caps per run. Zero infinite loops in 18 months. Architectural prevention, not hope.
The stack we ship every LangGraph system on.
LangGraph for control, LangSmith for sight, MCP for tools, and the AWS layer that runs the system.
T1
What we ship every LangGraph system on
LangGraph + LangSmithLangGraphLangChainLangSmithMCPPython (FastAPI)Node.js + ExpressMongoDBClaude Sonnet 4.6Claude HaikuGPT-5MixpanelPytest + Vitest
T2
When your brief calls for it
reach when neededClaude Haiku 4.5Llama / MixtralWeaviate / QdrantPostgreSQL + pgvectorCrewAIHuggingFace
T3
The cloud + infra
AWS-defaultAWSAWS BedrockAWS LambdaAWS Step FunctionsKubernetes (EKS)DockerRedisSQS / EventBridgeApache KafkaTerraformDatadogWebSockets
What AI leads actually ask before signing
Pain-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 and observability discipline stays the same.
We pin versions, ship with a tested upgrade path, and include the eval gold set so you can verify any future upgrade against it before deploying. We’ve done five LangChain major-version migrations in the last 18 months. None broke a client’s production agent.
Claude Sonnet 4.6 by default: tool-use discipline is mature and cost is reasonable. Claude Opus for complex reasoning nodes. GPT-5 where vision matters. Always behind a gateway so you can swap per node.
Yes, that’s the point of the graph structure. Reading the graph tells you what the agent does. Most teams take over fully within 3-6 months. We stay on a £5K/month advisory retainer if you want a second senior voice for the calls that come up.
Eval gold set of 100-300 real tasks with known correct answers, built with your team in week one, run in LangSmith on every PR. Pass rate is a number you both watch. Below threshold, the deploy is blocked.
30-day walk-away both ways. Milestone billing 25/25/25/25. UK VAT registered, listed on Companies House, shipping since 2019.
Every LangChain 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. No cancellation fee.
In context
What it looks like shipped.
langchain development, in context — the dashboards, flows and components your team actually ships, reviews and maintains.
One paragraph. That’s it.
Tell us what the agent should do, what data it needs, and what your gold set looks like (or doesn’t yet). Mohit replies inside 24 hours: a clear yes, a clear no, or the one question that decides it.
What happens after the email lands
- < 24h
A personal reply.
Yes, no, or the deciding question. Straight to your inbox.
- Week 1
The graph + gold set.
We map the state machine and build the eval set with your team.
- Wk 8
A system you can defend.
Typed graph, LangSmith traces, step budget. No more weekend loops.