01
Agentic Workflow Design
We map your business process into a LangGraph state machine - nodes for actions, edges for decisions. Every path is explicit, testable, and auditable. No black-box autonomy.
LangGraphPythonState MachinesGraph Visualization
LangGraph: the framework behind production agents.
Agents that work in production need more than prompt engineering: stateful workflows, fault tolerance, and human oversight. Laava builds every agentic application on LangGraph, the graph-based framework for reliable, controllable agents.
One workflow to production. One platform to build on what works.
Positioning
Why Most AI Agent Frameworks Fall Short in Production
The AI agent landscape is crowded with frameworks that demo well but break in production. Simple chain-based approaches can't handle the branching, looping, and error recovery that real business processes demand.
No-code tools lack version control and testability. And "autonomous" agent frameworks give you impressive demos but no control over what happens when things go wrong.
Enterprise AI agents need a different foundation - one built for complexity, reliability, and human oversight. That's the gap LangGraph fills, and why Laava standardized on it.
- Start with the bottleneck before tooling
- Proof before scale or transformation
- Decisions based on operational reality
Outcomes
What LangGraph Enables for Your Business
Complex multi-step business processes automated with full auditability
AI agents that recover gracefully from failures - no lost work
Human approval gates where your business needs them (Shadow Mode)
Real-time streaming responses for interactive user experiences
Switch LLM providers (OpenAI, Anthropic, Azure) without rewriting agents
Full graph visualization for debugging, testing, and compliance
How We Build with LangGraph
LangGraph powers the agent runtime: stateful workflows, retries, and human review.
02
Tool & Integration Orchestration
Your agent needs to call APIs, query databases, process documents, and trigger actions. LangGraph's tool-calling architecture connects to your existing systems cleanly - CRM, ERP, document stores.
LangGraph ToolsREST APIsDatabase ConnectorsMCP
03
Multi-Agent Orchestration
Complex problems need multiple specialized agents working together. LangGraph enables supervisor patterns, collaborative workflows, and agent-to-agent communication - all with shared state.
LangGraph Multi-AgentSupervisor PatternShared State
04
Production Hardening
We deploy LangGraph agents with checkpointing, persistence, streaming, and monitoring. Your agent recovers from crashes, scales under load, and provides full observability.
LangGraph PersistenceKubernetesLangfuseTerraform
Approach
Why LangGraph Over the Alternatives
We evaluated every major agent framework. Here's why LangGraph won.
Step 01
vs. Simple LangChain Chains
Basic LLM chains work for linear tasks, but real business processes have loops, branches, and conditional logic. LangGraph adds cycles and state management to LangChain's ecosystem - giving us the full power of graph-based orchestration without leaving a battle-tested stack.
Step 02
vs. AutoGen & CrewAI
These frameworks prioritize autonomous agent communication - impressive in demos, unpredictable in production. LangGraph gives us explicit control over every transition. We define exactly when agents act, when humans review, and how failures are handled. Less magic, more reliability.
Step 03
vs. Custom-Built Frameworks
We could build our own orchestration layer. But LangGraph is maintained by a large team, used by thousands of companies, and evolving fast. We'd rather spend our engineering time on your business logic than reinventing state management and persistence.
Step 04
vs. No-Code AI Tools
Tools like n8n and Make.com have AI features, but they lack version control, automated testing, and the flexibility to handle complex reasoning chains. LangGraph is code-first: every agent is testable, reviewable, and deployable through CI/CD - the way production software should be.
Principles
Our LangGraph Principles
Principle 01
Boring Excellence Over Hype
LangGraph isn't the flashiest framework - it's the most reliable. We choose proven tools that work at 3 AM on a Sunday, not tools that generate Twitter impressions. Every component in our stack earns its place through production performance.
Principle 02
Explicit Over Autonomous
We don't build agents that 'figure it out.' Every decision path is designed, every fallback is planned, and every critical action has a human gate. LangGraph's graph architecture makes this natural - you can see and verify every possible execution path.
Principle 03
Shadow Mode First
New agents start in Shadow Mode: they observe, suggest, and learn - but a human approves every action. LangGraph's human-in-the-loop support and checkpointing make this seamless. Autonomy is earned gradually, never assumed.
Principle 04
Model-Agnostic by Design
LangGraph works with any LLM provider. Today we might use Claude for reasoning and Llama for analysis. Tomorrow we might switch entirely. Your agent architecture stays the same - only the model configuration changes.
FAQ
What teams ask about agentic apps
The most practical questions that usually come up before a first application actually lands in the operation.
View all questionsWhat is LangGraph and how is it different from LangChain?
LangChain provides tools for working with LLMs - prompts, retrievers, chains. LangGraph extends this with a graph-based orchestration layer for building stateful, multi-step AI agent workflows. Think of LangChain as the toolkit and LangGraph as the workflow engine that coordinates how those tools work together in complex business processes.
Is LangGraph production-ready for enterprise use?
Yes. LangGraph includes built-in persistence (checkpointing), streaming, fault tolerance, and human-in-the-loop support. It's used in production by companies ranging from startups to Fortune 500 enterprises. Laava deploys LangGraph agents on Kubernetes with full observability, monitoring, and automated scaling.
Can I switch LLM providers later without rebuilding my agent?
Absolutely. LangGraph is model-agnostic by design. Your agent's workflow logic - the graph of nodes, edges, and state - is independent of the LLM provider. Switching from OpenAI to Anthropic or Azure OpenAI is a configuration change, not a rewrite.
How does human-in-the-loop work with LangGraph?
LangGraph supports interrupt points where the workflow pauses and waits for human input. At Laava, we use this for Shadow Mode: agents propose actions, and humans approve or correct them. Over time, as confidence grows, you can reduce the approval gates. The agent's state is persisted, so nothing is lost during the pause.
First step
Automate a complex process, reliably.
Bring your most complex process. We show how LangGraph on the Laava Platform automates it, reliably and under your control.
Response time
We typically respond within 24 hours