Typed state machines, durable checkpoints, eval harnesses in CI, LangSmith observability and AU integrations — the engineering you’d expect for any production service, applied to AI agents.
You're stringing together LangChain runnables and reaching for global variables to track state. The workflow is a state machine; it should be modelled as one.
Long-running agents fail mid-flow. You want durable state, replayable runs and the ability to resume from the last good checkpoint — not restart from scratch.
Confidence floors, policy triggers and escalation paths need to route ambiguous decisions to a human reviewer with full context — and resume cleanly when they respond.
You have specialist agents — research, draft, review, claim-check — and the handoffs between them keep breaking. You need A2A (agent-to-agent) handoffs done properly.
You want versioned golden datasets, regression suites that block the merge, and model A/B between Claude Sonnet 4.6, Opus 4.7, GPT-4o and DeepSeek-V3 on the same rubric.
LangSmith for traces, OpenTelemetry across the graph, p50/p95/p99 per node, cost-per-conversation dashboards. Not 'we'll check the logs when something breaks'.
System map, ROI hypotheses, eval-harness scope and a written architecture brief before code ships. We decide LangGraph vs LangChain vs CrewAI vs custom at this stage, on the workload.
Typed state schema, node graph, tool contracts, fallback chains, human-in-the-loop boundaries, model-selection policy, AU compliance touchpoints designed in.
Vertical-slice delivery: a thin end-to-end path lands first, then breadth. LangGraph runtime, typed tools, Zod / Pydantic schemas on every structured output, audit logging.
Golden datasets per intent, regression suites in CI, model A/B, hallucination tracking, tool-use accuracy scoring. Same rubric used in staging and on sampled production traffic.
Canary release behind feature flags, LangSmith + OpenTelemetry tracing live, dashboards, on-call playbooks. We watch the first 100 real conversations with your team.
$3K+ MRR covering ops, eval runs on every prompt or model change, drift detection, monthly architecture review and the on-call rotation for production incidents.
LangGraph state machine in front of Retell or Vapi handles triage, booking and AHPRA-aware refusals; Sonnet 4.6 primary with a Haiku 4.5 fallback; HotDoc and Cliniko write-back via typed tools.
Research → outline → draft → claim-check → edit, as a LangGraph supervisor pattern. Opus 4.7 for long-form, Sonnet 4.6 for editing, retrieval grounded against a curated corpus; brand-voice eval blocks publication.
LangGraph joins HubSpot deals with Stripe and product analytics, runs a GPT-4o-mini scoring node, writes a structured outcome to a HubSpot custom object. Eval harness tracks score drift weekly.
LangGraph orchestrates document collection, ID verification, BID evidence and AML red-flag detection. Opus 4.7 for the risk-classification node; human-in-the-loop escalation on anything ambiguous.
LangGraph routes inbound tickets through a classifier, drafts responses from a curated knowledge base, escalates claim-sensitive content. Hallucination rate tracked per intent and per model.
LangGraph drives document review, cross-system reconciliation against Xero, weekly partner reports. Snapshot tests prevent format drift; sampled production traces feed the regression set.
Shakan LangGraph engagements start at $20K+ for implementation (typically 4–10 weeks) and $3K+ MRR for ongoing operations, eval runs and model upgrades. We’ll tell you honestly when LangChain alone, n8n, or a managed agent platform is the better economic answer.
LangChain is excellent for composing primitives. For stateful, multi-step flows that must be observable, replayable and testable, LangGraph adds what's missing: typed state, deterministic transitions, durable checkpoints, first-class human-in-the-loop nodes. We still use LangChain components inside LangGraph nodes where they make sense — the two aren't mutually exclusive. For simple linear chains, LangChain alone is the right call.
The graph lives in git like any other code. Prompts are versioned files tied to the eval run that approved them. State schemas are typed (Pydantic or Zod) and reviewed alongside the graph. Every production deploy is from a tagged commit; the prompt registry tracks which prompt hash ran for which trace. No more 'someone edited it in the UI on Friday'.
Always. Every LangGraph engagement ships with a versioned golden dataset (50–500 examples per intent), a scoring rubric mixing deterministic and model-graded checks, a regression suite that blocks merges on agreed thresholds, and a model A/B harness covering Claude Sonnet 4.6, Opus 4.7, Haiku 4.5, GPT-4o, GPT-4o-mini and DeepSeek-V3. LangSmith is the default surface; we'll integrate with whatever your team already uses.
LangGraph's checkpointer (Postgres or Redis-backed in our deployments) persists state at every node transition. Mid-flow failures resume from the last good checkpoint rather than restarting. Human-in-the-loop pauses are durable — a reviewer can respond hours later and the run resumes cleanly. We treat replayability as a first-class architectural concern, not a nice-to-have.
Yes. LangGraph deploys as a Python or JS/TS service in your cloud account (AWS, GCP, Azure, AU regions where required). State persistence on your Postgres; vector memory on Pinecone, Weaviate, or pgvector — your choice. LangSmith is the standard observability layer; we'll wire OpenTelemetry to Datadog or Sentry alongside it.
$20K+ implementation, typically 4–10 weeks, scoped against a measurable revenue or cost line. $3K+ MRR retainer covering ops, eval runs, model upgrades, drift detection, dashboards and a monthly architecture review. Source escrow available; you own the code, the prompts, the evals, and the infrastructure.
45 minutes with a senior architect. We’ll pressure-test your current architecture or proposed design, identify failure modes worth fixing first, and show you what an eval harness for your workload looks like.