We use LangChain to compose agentic workflows and automations across your delivery and operations.
We reach for LangChain when a workflow has several dependent steps — retrieve, decide, act, verify — and needs to be observable and testable rather than a single opaque prompt.
When a workflow outgrows a single prompt
Plenty of AI features are one question and one answer. The interesting ones are not: retrieve a record, decide which path applies, call a tool, check the result, escalate if it fails. Expressed as one enormous prompt, that logic becomes impossible to test and impossible to debug when it misbehaves.
LangChain gives that logic structure. Steps are explicit, composable and individually testable, which means a failure can be located rather than guessed at. For anything we expect to run unattended, that observability is not optional.
How we use it
We keep chains as simple as the problem allows — most business workflows need three or four deliberate steps, not an elaborate autonomous agent. Each step is instrumented so we can see inputs, outputs and timing in production, and each has defined behaviour when the step fails: retry, fall back, or hand to a human.
Being model-agnostic matters commercially. The frontier model changes faster than your processes do, and a workflow built to swap providers protects you from both price movements and capability shifts.
Discipline over autonomy
The temptation with agent frameworks is to grant broad autonomy and hope. Industry outcomes through 2026 are unambiguous about where that leads — Gartner forecasts more than 40% of agentic projects cancelled by 2027, largely on unclear value and inadequate controls. We scope narrowly, define what the agent may decide alone, evaluate before launch, and keep a tested off switch.
Does this lock us into one framework?
We use orchestration where it earns its place and plain code where it does not. The retrieval design, prompts, evaluations and data contracts are the durable assets; the orchestration layer is deliberately the most replaceable part of the stack.
Who maintains it after launch?
Whoever you choose — us on a support arrangement, or your own team. We hand over documented pipelines, the evaluation suite and the prompts, because an AI feature nobody can safely change is a liability rather than an asset.
How do you test something non-deterministic?
With an evaluation suite of real inputs and expected behaviours, run on every change. We measure grounding, refusal correctness and regression against known-good answers, so a prompt tweak cannot quietly degrade quality.