AI Agents

AI Agent Frameworks: LangGraph, AutoGen, CrewAI

AI agent framework comparison: LangGraph, AutoGen, CrewAI for autonomous multi-step workflows. Production patterns, guardrails, evaluation.

Build Production AI Agents Without Hype

AI agent frameworks (LangGraph, AutoGen, CrewAI) enable autonomous multi-step workflows: research, document processing, support triage, sales follow-up. Production deployment requires guardrails, evaluation, and human-in-loop discipline. We help engineering teams ship agent capabilities that compound rather than become science projects.

Key Capabilities

01

LangGraph

LangChain stateful graph framework for agent orchestration.

02

Microsoft AutoGen

Multi-agent conversation framework with strong research orientation.

03

CrewAI

Role-based agent framework for sequential and parallel agent tasks.

04

Guardrails Stack

Input validation, output validation, tool authorization, human approval gates.

05

Evaluation Frameworks

Trajectory evaluation, task success metrics, regression testing.

06

Production Patterns

Observability, retry logic, fallback paths, cost management.

3+
Agent Frameworks
Production-Ready
Patterns
4-Layer
Guardrails
4.6/5
Engineering NPS

Process

01

Use Case Selection

Narrow well-bounded workflows for first agent deployments.

02

Architecture

Framework selection plus guardrails design.

03

Build & Evaluate

Build with evaluation pipeline.

04

Production

Deploy with observability and human-in-loop where needed.

Benefits

Production Capability

Move from agent demos to production capabilities.

Cost Discipline

Caching, model selection, evaluation prevent agent cost runaway.

Human-in-Loop

Approval gates manage risk for high-stakes workflows.

Compounding Value

Reusable agent components compound across use cases.

Tools & Tech

  • LangGraph
  • LangChain
  • AutoGen
  • CrewAI
  • LangSmith

Industries

  • SaaS
  • Financial Services
  • Healthcare
  • Manufacturing
  • Retail
  • Energy

FAQ

LangGraph vs AutoGen?
LangGraph for complex stateful workflows. AutoGen stronger for research-style multi-agent. CrewAI for role-based teams.
Production-ready?
For narrow well-bounded workflows yes. Multi-agent autonomy at scale still maturing.
Guardrails essential?
Yes. Input/output validation, tool authorization, approval gates, observability all required.
Cost management?
Caching, model fallbacks (start with smaller model), trajectory cost tracking. Without discipline, agents 10x base cost.

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