Building an AI agent demo takes a weekend. Shipping one to production takes months. That gap, between "it works on my laptop" and "it runs our business process reliably", is where most AI agent projects go to die. After building production AI agents for enterprise clients across industries, we've developed strong opinions about what it takes to bridge that gap. This post explains why we chose LangGraph as the orchestration layer for our agentic AI work, and what we've learned deploying it in the real world.
The Production Gap: Why Most Agent Frameworks Fall Short
The AI agent ecosystem is booming. OpenAI's Assistants API, Microsoft's AutoGen, CrewAI, Amazon Bedrock Agents — the list grows every month. Each promises to make building agents easy. And they do, for demos.
The problem surfaces when you move beyond the happy path. In production, AI agents need to handle scenarios that demo frameworks quietly ignore:
- What happens when an LLM call times out mid-workflow?
- How do you pause a multi-step process for human approval, then resume it days later?
- How do you trace exactly why an agent made a particular decision at 3 AM last Tuesday?
- What happens when the same agent needs to handle 500 concurrent conversations, each with unique state?
- How do you roll back when an agent takes a wrong turn four steps ago?
Most frameworks treat these as edge cases. We treat them as requirements. When your AI agent is approving invoices, routing support tickets, or managing compliance workflows, "it usually works" is not an acceptable standard.
Chains vs. Graphs: The Architecture That Changes Everything
The fundamental limitation of earlier frameworks like LangChain's original chains is their linearity. A chain runs step A, then step B, then step C. It's a pipeline. This works beautifully for straightforward tasks: retrieve documents, stuff them into a prompt, generate an answer.
But real business processes are not pipelines. They're graphs. Consider a customer onboarding agent. It needs to:
- Verify identity documents (might fail, needs retry with different verification service)
- Run a compliance check (might need human review if flagged)
- Set up the account (depends on the outcome of both previous steps)
- Send a welcome email (only if everything passed)
- Route to a human agent (if any step fails after retries)
This workflow has branches, loops, conditional paths, and points where it needs to wait for external input. A linear chain simply cannot express this. You end up hacking around the framework's limitations, writing glue code that becomes the actual complexity of your system.
LangGraph models workflows as directed graphs. Nodes represent processing steps — calling an LLM, invoking a tool, transforming data. Edges define transitions between steps, and they can be conditional: "if the compliance check returned 'flagged', route to the human review node; otherwise, proceed to account setup." This maps directly to how business processes actually work.
What LangGraph Actually Gives You
Durable Execution and Checkpointing
This is the feature that separates LangGraph from nearly everything else. Every time your graph transitions from one node to another, LangGraph can save a checkpoint — a complete snapshot of the agent's state. This checkpoint is persisted to a database (PostgreSQL in our case), not just held in memory.
Why does this matter? Three reasons:
- Fault tolerance. If a node fails, you don't re-run the entire workflow from scratch. You resume from the last checkpoint. When your agent is ten steps into a complex process and the LLM provider has a hiccup, you pick up right where you left off.
- Long-running processes. Some business workflows take hours or days. An approval process might sit waiting for a manager's sign-off for a week. LangGraph persists the state, frees the compute resources, and resumes seamlessly when the approval comes in.
- Auditability. Every checkpoint is a verifiable record of the agent's state at each decision point. When a client asks "why did the agent do X?", you can replay the exact sequence of states and decisions.
LangGraph offers three durability modes — sync, async, and exit — letting you balance between maximum durability and performance. For critical workflows, we use sync mode (checkpoint before every step). For high-throughput, less critical flows, async mode gives us durability with minimal latency overhead.
Human-in-the-Loop: Where AI Meets Reality
This is not a nice-to-have. For enterprise AI agents, human oversight is a hard requirement. The EU AI Act mandates human oversight for high-risk AI systems. Financial regulators require approval workflows. And frankly, LLMs are not reliable enough to run unsupervised on consequential business decisions.
LangGraph's interrupt() mechanism is elegant. At any point in your graph, you can pause execution, surface information to a human reviewer, and wait — indefinitely if needed — for their input. When they respond, the graph resumes exactly where it paused, with the human's input injected into the state.
We implement this as what we call Shadow Mode: the agent processes a request and prepares its proposed action, but instead of executing it, it pauses and presents the action to a human for approval. The human can approve, modify, or reject. Over time, as trust is established, you can gradually expand the agent's autonomy — letting it execute low-risk actions independently while still requiring approval for high-stakes ones.
This is fundamentally different from frameworks like CrewAI or basic AutoGen setups, where human interaction is an afterthought bolted on top. In LangGraph, interrupts are a first-class primitive baked into the execution model.
Stateful Memory: Short-Term and Long-Term
AI agents without memory are chatbots with extra steps. Real business agents need to remember context within a conversation (short-term memory) and across conversations (long-term memory).
LangGraph handles short-term memory through its state management. Each thread (conversation) has its own state, persisted via checkpoints. This is not just message history — it includes any structured data your workflow needs: uploaded documents, intermediate results, user preferences, approval statuses.
For long-term memory, LangGraph provides stores that persist data across threads with custom namespaces. An agent assisting a returning customer can recall their previous interactions, preferences, and history — drawing on semantic memory (facts about the user), episodic memory (what happened in past interactions), and procedural memory (learned rules for handling this type of request).
Why LangGraph Over the Alternatives
We evaluated the major options. Here is our honest assessment:
OpenAI Assistants API is the easiest to start with. It handles threading, tool calling, and file retrieval out of the box. But it locks you into OpenAI models, runs on their infrastructure, and gives you limited control over execution flow. When you need conditional branching, custom state management, or the ability to run on your own infrastructure with your own models, it quickly becomes a constraint.
AutoGen (Microsoft) excels at multi-agent conversation patterns. Agents can chat with each other, delegate tasks, and collaborate. But for structured business workflows that need deterministic routing, persistent state, and human-in-the-loop at specific decision points, AutoGen's conversation-centric model requires significant additional engineering.
CrewAI offers a beautiful abstraction with roles, goals, and task delegation. It's great for content generation and research workflows where agents collaborate autonomously. But its higher-level abstractions can become limiting when you need fine-grained control over execution flow, fault tolerance, or integration with complex enterprise systems.
Amazon Bedrock Agents provides managed agent infrastructure on AWS, with built-in integration to AWS services. Strong for teams deeply invested in the AWS ecosystem. But the vendor lock-in is significant, customization options are limited, and you're constrained to models available on Bedrock.
LangGraph sits at the right level of abstraction for us. It's low-level enough to give us full control over every decision point, state transition, and failure handler. Yet it's structured enough to prevent the sprawl you get when building from scratch. Critically, it's model-agnostic and infrastructure-agnostic — we can run Claude, GPT-4, Mistral, or open-source models, deployed on any cloud or on-premises. For our clients who need sovereign AI or data residency compliance, this flexibility is essential.
Patterns We Use in Production
Over multiple deployments, we've converged on a set of patterns that work reliably:
Conditional Routing with Tool Calling
The most common pattern is an agent that reasons about a user's request, selects the appropriate tool, executes it, evaluates the result, and decides the next step. In LangGraph, this is a cycle: the LLM node decides to call a tool, a conditional edge routes to the tool execution node, the result feeds back to the LLM, and the cycle continues until the LLM decides it has a final answer.
The key advantage over simple tool-calling APIs is that you control every step. You can add validation between the LLM's tool call decision and actual execution. You can log every tool invocation. You can implement rate limiting, cost controls, or circuit breakers — all as nodes in the graph.
Multi-Agent Orchestration
For complex workflows, a single agent with many tools becomes unwieldy. We use a supervisor pattern: a coordinating agent that delegates to specialised sub-agents. Each sub-agent is itself a LangGraph graph, with its own state and tools, composed into the larger workflow as a subgraph.
For example, an enterprise document processing pipeline might have a classification agent, an extraction agent, a validation agent, and a routing agent. The supervisor decides which agent handles each stage, and the graph structure ensures proper data flow and error handling between them.
Graceful Degradation
In production, things go wrong. APIs time out. Models hallucinate. External services return unexpected data. We build fallback paths directly into the graph: if the primary model fails, route to a backup model. If a tool call returns invalid data, route to a validation node that attempts correction before surfacing an error. Every graph has explicit error-handling edges, not just try-catch blocks in application code.
LangGraph in Laava's 3 Layer Architecture
At Laava, we structure AI solutions using a three-layer architecture:
- Context Layer (Metadata) — structured data, vector stores, knowledge bases that give the agent grounded, relevant information.
- Reasoning Layer (Brain) — where LangGraph lives. This is the orchestration that turns raw intelligence into structured decision-making. The graph defines what the agent can do, in what order, under what conditions, and with what safeguards.
- Action Layer (Integration) — APIs, databases, external systems that the agent actually interacts with. Tool integrations, webhooks, CRM updates, document generation.
LangGraph is the backbone of the Reasoning Layer. It connects the intelligence of the LLM to the context it needs and the actions it can take. Without solid orchestration, you either have a chatbot that knows things but can't act on them, or an automation that acts without reasoning. The graph structure is what makes it an agent — a system that perceives, reasons, decides, and acts in a loop.
The Boring Truth About Production AI
There's no magic in building production AI agents. It's software engineering. Good state management, proper error handling, observability, testing, and deployment pipelines. LangGraph doesn't magically solve all of this, but it provides the right primitives to build on: durable state, interruptible execution, composable graphs, and model-agnostic design.
The teams that succeed with AI agents in production are the ones that treat them as software systems first and AI experiments second. That means version control for your graphs, integration tests for your workflows, monitoring for your checkpoints, and gradual rollout with human oversight.
We chose LangGraph because it aligns with this engineering-first philosophy. It doesn't hide complexity behind abstractions that break in production. It gives you the building blocks and gets out of the way.
Building Production AI Agents?
If you're moving beyond proof-of-concept and need AI agents that actually run your business processes reliably, we'd like to talk. We bring production experience with LangGraph, a model-agnostic architecture, and a structured approach that gets from pilot to production in weeks, not months. Learn more about our LangGraph-based agent development on our LangGraph solutions page.