Production AI Workflows & Multi-Agent Blueprints

Learn how to extract structured intelligence from complex PDFs, images, and scanned documents in real-time using cutting-edge OCR and LlamaIndex orchestration.

Modern cyber threats outpace human response times. Discover how to build a distributed Cyber Threat Intelligence (CTI) gateway using CrewAI, where specialized autonomous agents monitor security feeds, analyze anomalous network patterns, and execute automated remediation hooks to isolate compromised systems instantly.

Cloud and LLM API costs can spiral out of control without constant monitoring. Learn how to deploy an autonomous FinOps AI agent that continuously analyzes AWS CloudWatch metrics, tracks LiteLLM usage, and automatically scales infrastructure or routes models to optimize enterprise spend.

A deep dive into architecting an enterprise-grade autonomous software engineering pipeline. Learn how to combine AutoGen 0.4's multi-agent capabilities with SonarQube's Abstract Syntax Tree (AST) analysis to create a self-correcting loop that generates, audits, and fixes code without human intervention.

Embodied AI is moving out of the lab. This workflow shows how to wire a VLA model (vision frames in, natural-language commands in, low-level action tokens out) into a warehouse pick-and-place robot with real-time budgets, safety interlocks, and full telemetry.

Architect a LangGraph-powered orchestrator that polls real-time wholesale energy markets (PJM, ERCOT), forecasts prices, and time-shifts or migrates AI workloads to the cheapest, greenest compute region - including SMR-powered data centers.

Give agents durable memory. Combine mem0 as the universal memory layer with LangGraph orchestration and a vector store to persist user, session, and agent-level context across sessions, channels, and devices, with adaptive updates that avoid memory bloat.

Engineer a real-time voice agent that streams speech-to-text, orchestrates an LLM with tool calling, and synthesizes speech so the conversational loop stays under 300ms perceived latency, with confidence thresholds and human escalation as safety nets.

Move beyond retrieve-then-generate. Build a self-correcting agentic graph RAG pipeline in LangGraph that fuses vector search with a knowledge graph, decomposes complex queries into sub-goals, performs multi-hop reasoning, grades every retrieved chunk, and rewrites queries in a retry loop until the evidence is solid.

Make LangGraph agents durable and auditable with Postgres checkpointing, Pydantic event-sourced state, crash recovery, and time-travel replay. This workflow does not just survive a crash, it rewinds to any past state and forks a new future.

Agents speak two protocols: MCP for tools and A2A for agents. This workflow builds a FastAPI interoperability gateway that bridges Google ADK and LangGraph, exposing A2A inbound while multiplexing shared MCP tool servers to both frameworks.

Production-grade observability for multi-agent systems needs three layers: OpenTelemetry for standardized signal emission, OpenInference for LLM-aware semantics, and Langfuse for trace analytics. This workflow wires all three into a CrewAI or LangGraph fleet without forking your agents.