AI Workflows & Agentic Architecture Directory

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.

Three agent communication protocols are battling for dominance in 2026: Google's A2A for agent-to-agent, Anthropic's MCP for tool access, and the Linux Foundation's Agent Plugins for portable skills. Here's how they compare, where they overlap, and the convergence pattern that's winning.

Running 1,000 concurrent AI agents at GPT-5.6 Sol costs $47,400/month. With intelligent model routing, semantic caching, and tiered deployment, that drops to $3,200/month — a 93% reduction. Here's the complete cost breakdown and the routing strategies making it possible.

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.

Schema drift, silent data corruption, and downstream failures cost enterprises $4.7M annually. This workflow deploys LangGraph-powered anomaly detection with Temporal durable execution to automatically detect, diagnose, and repair data pipeline failures without human intervention.

The 2026 AI model market has crystallized into three distinct pricing tiers — Fast ($0.14/M), Balanced ($3/M), and Premium ($15/M) — but most teams still budget using a single model's price. This deep dive breaks down the real cost structure of AI agent fleets, introduces a cost-per-task-modeling framework, and shows how the top 10% of cost-efficient teams spend 73% less per agent invocation while maintaining quality.

ML teams waste 40% of engineering time rebuilding feature pipelines that already exist. This FastMCP TypeScript server wraps Feast and Redis to expose real-time and batch features to AI agents via MCP, enabling Claude Desktop and Cursor to query feature vectors, detect drift, and trigger retraining — all with sub-5ms P99 latency.

Data analysts spend 60% of their time rediscovering which dbt models exist and how they connect. This FastMCP TypeScript server exposes the dbt Semantic Layer to AI agents, enabling Claude Desktop and Cursor to query metrics, trace lineage, validate models, and trigger incremental runs — reducing data transformation cycle time from days to minutes.

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.

Anthropic has launched Claude 5 Enterprise, its most capable model yet with a 2M token context window, native tool execution, and enterprise SSO — arriving just as the company confirms crossing $2B ARR. The release positions Anthropic as the enterprise AI leader against OpenAI's GPT-5.6 Sol and Google's Gemini 3.7 Pro.

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.