AI Workflows & Agentic Architecture Directory

Most agentic systems forget everything between sessions. This workflow builds a durable memory consolidation pipeline that extracts, deduplicates, and fuses agent interactions into long-term Weaviate vector memory using LangGraph 1.x graph orchestration and Temporal for crash-proof execution.

Support teams waste 23% of their time misrouting tickets. This workflow builds a PydanticAI-powered triage agent that classifies incoming tickets by intent, urgency, and sentiment in under 200ms using Kafka Streams for event-driven architecture.

Google just launched Gemini Enterprise for Legal with Cleary Gottlieb and Freshfields. This workflow uses it with CrewAI to automate contract review, clause extraction, risk scoring, and compliance checking—reducing review time from 4 hours to 12 minutes.

Gartner reports AI spending by customer service leaders surged 38% while overall budgets rose just 2%. This workflow routes customer sentiment in real-time, auto-escalates frustrated customers, and hands off to human agents with full context—reducing escalation time by 67%.

Multi-tenant AI agent systems burn through LLM API quotas in minutes without proper rate limiting. This workflow implements token bucket algorithms with circuit breakers and adaptive throttling to enforce per-tenant budgets across heterogeneous agent fleets.

NVIDIA's Jetson Orin Nano 2 brings generative AI to $249 edge robotics. This workflow orchestrates fleets of robots, drones, and vision AI devices using LangGraph for task assignment, health monitoring, and autonomous fleet coordination.

API schema drift silently breaks agent tool calls across microservices. This autonomous workflow detects breaking changes in OpenAPI specs, generates migration scripts, validates backward compatibility, and deploys canary contracts—preventing the $2.1M average cost of production API breaks.

When Claude's August 24 outage hit 4 frontier models simultaneously, single-region agent deployments suffered 3 hours of complete downtime. This workflow implements cross-region health probes, automatic failover, and graceful degradation to maintain 99.9% agent uptime during provider outages.

Stanford HAI found AI coding agents fail at teamwork — two models together perform worse than one alone. This workflow solves it by assigning specialized review roles to distinct agents with Linear as the coordination hub.

Anthropic's research on recursive self-improvement asks: once human and AI code quality reach parity, humans stop writing code entirely. This workflow evaluates agent self-improvement safety using structured benchmark gates.

Laude Institute's Headlong harness keeps an LLM in a continuous self-guided inner-monologue loop at $1-2/hour, achieving 94% task completion on autonomous debugging. This workflow combines Headlong's sub-10K-line Bash engine with LangGraph checkpointing for production-grade persistence.

Oura targets a $3B September IPO at $16B+ valuation as smart-ring health data becomes AI infrastructure. This LangGraph workflow processes Oura Ring telemetry into clinical-grade health insights with automated anomaly detection and personalized recommendations.