LangChain
Engineering.
We build and productionize LangChain and LangGraph applications, turning notebook chains into stateful, observable agent systems that survive real traffic and real failure modes.
From Chain to State Machine
Most LangChain prototypes are written as a linear sequence of prompt calls, which works fine on a laptop and falls apart the first time a tool call times out, a user interrupts mid-flow, or two requests hit shared state at once. We rebuild these as LangGraph state machines: every step is a node with an explicit input/output schema, every transition is an edge with defined failure handling, and the whole graph is checkpointed so a crashed run resumes instead of restarting from scratch.
Checkpointed graph state (Postgres or Redis-backed) so a failed run resumes from its last successful node instead of re-executing from the start.
Structured output parsing with Pydantic/Zod schemas at every LLM call boundary, so a malformed completion fails loudly instead of silently corrupting downstream state.
Human-in-the-loop interrupts modeled as first-class graph nodes, not bolted-on webhook callbacks.
Per-node token and latency budgets enforced at the graph level, so one slow tool call can't blow the whole request's SLA.
What we build
Stateful Agent Orchestration
Migrating brittle chain-of-prompts scripts to LangGraph state machines with explicit retries, checkpoints, and human-in-the-loop steps that survive process restarts mid-run.
Tool-Use Hardening
Wrapping function-calling tools with strict JSON-schema validation, timeouts, circuit breakers, and idempotency keys so a hallucinated or malformed call can't corrupt a downstream system.
Observability & Tracing
Instrumenting every chain and graph node with LangSmith or self-hosted Langfuse tracing, mapped to OpenTelemetry spans, so a bad output is debuggable back to the exact prompt, retrieval, and tool call that produced it.
Multi-Agent Coordination
Designing supervisor/worker agent topologies with explicit handoff contracts, so multi-agent debate or delegation has a deterministic termination condition instead of looping indefinitely.
What we fix
Chains that work in a notebook but fail silently under concurrent production load
No visibility into why an agent produced a given output, making incidents undebuggable
Unbounded retry loops that quietly burn through token budgets on transient tool failures
Tool calls with no schema validation, corrupting downstream systems on malformed output
Multi-agent setups that loop indefinitely with no explicit termination or escalation condition
State lost on process restart, forcing users to redo a multi-step interaction from scratch
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.