afterbuild/ops
§ PLATFORM/langchain-developer

What breaks when you ship a LangChain app

LangGraph developers who make agents behave in production. Explicit graphs, interrupts, human-in-the-loop, retries, parallel sub-agents — with LangSmith traces, eval suites, and guardrails so you can ship the agent without losing sleep.

48%
AI code vulnerability rate (Veracode 2025)
7
LangChain problem pages indexed
48h
Rescue diagnostic SLA
Free rescue diagnostic →Send us the repo
Quick verdict

LangChain / LangGraph engagements cover the seven places agent builds typically stall in production: agent runaway loops without step caps, no observability on agent runs (no LangSmith tracing wired), parallel sub-agents causing rate-limit storms, LangGraph state-management confusion, RAG retrieval noise hidden by confident-sounding answers, eval suites absent, and production deploys without interrupts or HITL. We build LangGraph agent graphs with branching, interrupts, retries, and parallel sub-agents where they help. Every build ships with LangSmith traces, an eval suite, and guardrails (max steps, max tokens, tool whitelist, per-session cost ceiling, kill-switch).

§ FAILURES/every way it ships broken

Every way LangChain ships broken code

LangChain and LangGraph are the most capable open-source agent frameworks in the space, and the most tempting to get wrong. The failure mode isn't 'the framework is bad'; it's that production agents need guardrails, observability, and evals that the 'hello world' tutorials skip. This page is for hiring senior LangGraph engineers who have shipped production agents against every one of those failure modes.

E-01✕ FAIL

Agent runaway loops

No recursion limit, no max-step cap, no kill-switch. A stuck agent burns $50+ per session before you notice. We wire recursionLimit, cost ceilings, and a runtime kill flag into every graph we ship.

E-02✕ FAIL

No observability on agent runs

LangSmith tracing is one environment variable away, and most builds we audit don't have it on. Without traces you can't debug agent behavior, can't track eval regressions, can't see where cost is going. We wire LangSmith (or Helicone / custom OpenTelemetry) on Day 1.

E-03✕ FAIL

Parallel sub-agents causing rate-limit storms

Fan-out without throttle hits the provider RPM ceiling within minutes. We add per-tier rate limiters, backoff, and semaphore-bounded concurrency so parallel sub-agents don't take each other down.

E-04✕ FAIL

LangGraph state management confusion

State channels, reducers, checkpoints, interrupts — the mental model is not obvious. We've seen teams maintain state by re-reading the full message history on every node and wondering why it's slow. We restructure state to what LangGraph expects.

E-05✕ FAIL

RAG retrieval noise hidden by confident answers

Without a reranker or an eval suite, retrieval returns plausible-but-wrong chunks and the agent confidently cites them. We add reranking (Cohere rerank-3 or cross-encoder), confidence thresholds, and source-citation requirements.

E-06✕ FAIL

Eval suite absent

If you're not running 20+ golden scenarios on every change, you're shipping regressions blind. We write the eval suite, wire it to LangSmith or a local runner, and gate deploys on the precision / recall numbers.

E-07✕ FAIL

Production deploys without interrupts or HITL

Any write that's hard to reverse (refund, account change, external API call with side effects) should pause for human approval. LangGraph's interrupt primitive makes this clean; most teams skip it and then firefight the first bad agent run.

§ RESCUE/from your app to production

From your LangChain app to production

The rescue path we run on every LangChain engagement. Fixed price, fixed scope, no hourly surprises.

  1. 0148h

    Free rescue diagnostic

    Send the repo. We audit the LangChain app — auth, DB, integrations, deploy — and return a written fix plan in 48 hours.

  2. 02Week 1

    Triage & stop-the-bleed

    Patch the highest-impact failure modes first — the RLS hole, the broken webhook, the OAuth loop. No feature work until production is safe.

  3. 03Week 2-3

    Hardening & test coverage

    Real migrations, signed webhooks, session management, error monitoring. Tests for every regression so LangChain prompts can't re-break them.

  4. 04Week 4

    Production handoff

    Deploy to a portable stack (Vercel / Fly / Railway), hand back a repo your next engineer can read, and stay on-call for 2 weeks.

§ COMPARE/other ai builders

LangChain compared to other AI builders

Evaluating LangChain against another tool, or moving between them? Start here.

Related platforms
Head-to-head comparisons
Related Build + Integrate services

Most LangGraph engagements route through one of these fixed-fee services.

AI Agent MVP — $9,499 / 4 weeks
Production LangGraph agent with tools, guardrails, observability, HITL, and eval suite.
RAG Build — $6,999 / 3 weeks
Production RAG with ingestion, vector DB, reranker, chat UI or API, and golden-set eval suite.
Build Custom AI Agent (solution)
Full solution page covering the LangGraph or Claude Agent SDK build path.
Build pillar overview
Every fixed-fee service we run for teams shipping agents, RAG, and automation.
§ PRICING/fixed price, fixed scope

LangChain rescue pricing

Three entry points. Every engagement is fixed-fee with a written scope — no hourly surprises, no per-credit gambling.

price
Free
turnaround
48 hours
scope
Written LangChain audit + fix plan
guarantee
No obligation
Book diagnostic→
most common
price
$299
turnaround
48 hours
scope
Emergency triage for a single critical failure
guarantee
Fix or refund
Triage now→
price
From $15k
turnaround
2–6 weeks
scope
Full LangChain rescue — auth, DB, integrations, deploy
guarantee
Fixed price
Start rescue→
When you need us
  • →An agent prototype works in the notebook and behaves badly in production — looping, slow, or unobserved
  • →You need a production multi-agent system and the LangGraph docs have stalled your team
  • →Your eval numbers are moving in the wrong direction and no one can explain why
  • →You're picking between LangGraph, Claude Agent SDK, and CrewAI and want a senior opinion
Stack we support
LangGraph (Python)LangGraph (TypeScript)LangChainLangSmithLangServePineconeChromaWeaviatepgvectorCohere rerank-3LlamaIndexOpenAI + Anthropic SDKs
§ FAQ/founders ask

LangChain questions founders ask

FAQ
LangGraph vs. Claude Agent SDK — when does each win?
LangGraph wins when the graph is explicit and observable — branching logic, interrupts for HITL, parallel sub-agents, multi-provider (OpenAI + Anthropic) swap. It's provider-agnostic and the traces-in-LangSmith story is excellent. Claude Agent SDK wins when you're Anthropic-native and want the built-in features (skills, memory, subagents, tight MCP integration) without hand-rolling them. We pick on the Day-1 scoping; many production builds use both (LangGraph for orchestration, Agent SDK inside specific nodes).
How do you prevent agent runaway?
Four layers. (1) recursionLimit on the graph — hard cap on node executions per run. (2) maxTokensPerRun — cap on total tokens across the session. (3) costCeilingUSD — cap on dollar spend per session. (4) Kill-switch env var — flip to drop all new sessions instantly while in-flight ones complete under their caps. A single pathological run costs cents, not hundreds of dollars.
Do you use LangSmith or something else for tracing?
LangSmith is our default — it's the native fit for LangGraph and the developer experience is excellent. Helicone is our second choice for teams that want a lighter OpenTelemetry-compatible surface. Custom OpenTelemetry when the team already runs a Datadog / Honeycomb / Grafana stack and wants agent traces in the same pane. We wire one of the three on Day 1 of every engagement.
Can we run LangGraph in production on AWS / GCP / self-hosted?
Yes. LangGraph deploys as a normal Python / Node service — Docker container, serverless function, or managed LangGraph Cloud. We've shipped production LangGraph on AWS Fargate, GCP Cloud Run, Vercel Functions, Fly.io, and self-hosted Kubernetes. Checkpoint storage is Postgres by default (the LangGraph PostgresSaver) so you bring your own DB. For teams on LangGraph Cloud, we wire the same guardrails and evals.
How do evals work with LangGraph?
LangSmith has native eval support — you run a dataset through your graph and it computes metrics against expected outputs. We typically ship a golden set of 20–50 scenarios per agent, run them on every PR, and gate deploys on precision / recall / hallucination rate. For agents with non-deterministic outputs we use LLM-as-judge evals (Claude Sonnet scoring Claude Haiku outputs, etc.) against a rubric we write on Day 1.
What's the typical engagement shape?
AI Agent MVP ($9,499 / 4 weeks) is the most common path — production LangGraph agent with 2–3 tools, guardrails, observability, HITL, and eval suite. RAG Build ($6,999 / 3 weeks) when the core need is retrieval with a reranker. AI Automation Sprint ($3,999 / 2 weeks) when the automation doesn't need full agent autonomy. We scope on Day 1.
About the author

Hyder Shah leads Afterbuild Labs, shipping production rescues for apps built in Lovable, Bolt.new, Cursor, v0, Replit Agent, Base44, Claude Code, and Windsurf — at fixed price.

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