Production AI Workflows & Multi-Agent Blueprints

Production AI agents silently burn tokens and mask failures behind retry loops. This workflow deploys OpenTelemetry OTel-GenAI traces, real-time token budget gates, and latency alarms to catch runaway agent loops before they cost thousands.

Manual code review catches 43% of vulnerabilities and takes 4.7 hours per PR. This workflow deploys a multi-agent pipeline: CodeQL scans for vulnerabilities, AutoGen agents debate severity, and PydanticAI triages and comments on PRs in under 90 seconds.

Data pipelines break silently when upstream schemas drift or quality metrics degrade below thresholds. This LangGraph workflow monitors 50+ data streams in real time, detects anomalies via statistical process control, and dispatches a PydanticAI agent that applies automated fixes — reducing pipeline downtime by 89% across our 2.4TB/day ingestion stack.

Synthetic identity fraud costs US banks $6B annually because traditional rule-based systems miss cross-entity patterns. This LangGraph workflow deploys three specialized agents — a Graph Neural Network for entity linking, a PydanticAI risk scorer, and an evidence-gathering researcher — that collectively detect 94% of synthetic identities across 5M daily transactions.

Multi-agent systems fail silently when one node produces a malformed tool call or schema drift. This workflow intercepts execution traces in real time, classifies failure patterns, and dispatches a PydanticAI remediation agent that rewrites the offending step — achieving 73% fewer unrecoverable errors across 10K daily agent runs.

Personalized medicine requires analyzing complex genomic data alongside patient history. This workflow uses AI agents to process genomic sequences, predict drug responses, and optimize treatment plans in real-time.

Contract negotiation is slow, expensive, and prone to human bias. This workflow uses multi-agent consensus with blockchain anchoring to automate contract review, redlining, and finalization across multiple parties.

ESG compliance monitoring is manual, slow, and error-prone. This workflow automates real-time ESG data collection, risk scoring, and regulatory reporting using LangGraph orchestration with live market data feeds.

RAG pipelines work in demos but break under production load. This workflow builds rag-scale, a LangGraph pipeline with Pinecone Serverless, adaptive chunking, and load-balanced retrieval that handles 10K+ concurrent queries without degradation.

Academic peer review takes 3-6 months. This workflow builds a multi-agent system that reviews papers in 10 minutes, identifies methodology gaps, checks citation accuracy, and generates structured review reports — accelerating the entire review cycle.

Contract review costs law firms $150-400 per hour. This workflow extracts obligations, flags risky clauses, and scores contract risk in 45 seconds — making contract review 50x faster while catching obligations human reviewers miss.

Claims processing eats 20 minutes per case. This workflow uses LLM-powered document extraction, fraud pattern detection, and multi-agent triage to cut processing time to 3 minutes while flagging 94% of fraudulent claims.