AI Workflows

When your agent fleet hits rate limits, naive retries amplify the problem 10x. This backpressure workflow prevents cascade failures with adaptive routing and retry budgets.

Teams generating synthetic training data for agents are hitting a wall: 68% report performance degradation within 90 days. This pipeline catches drift before it reaches production.

Most teams lose 3-5 hours debugging a single agent failure because their tracing stops at the LLM call. This pipeline restores full context with OpenTelemetry GenAI semantic conventions and real-time budget gates.

Data governance teams spend 60% of their time manually tracing lineage across data pipelines. This guide builds an autonomous governance agent that auto-discovers lineage, detects PII, and generates SOC2/GDPR audit reports using OpenLineage, Apache Atlas, and LangGraph.

Legal teams spend 72% of contract review time searching for comparable clauses in previous agreements. This CrewAI multi-agent workflow auto-ingests SEC filings, extracts negotiation benchmarks, and generates redline recommendations — cutting review cycles from 5 days to 45 minutes.

Production voice AI agents demand sub-200ms round-trip latency across WebSocket audio streams. This guide delivers a production-ready multi-file implementation with circuit breaker fallback and enterprise audio caching.

Production voice AI agents demand sub-200ms round-trip latency across WebSocket audio streams. This guide architecturally decomposes the OpenAI Realtime API + Twilio Media Streams pipeline, delivering a copy-pasteable multi-file implementation with circuit breaker fallback, VAD tuning, and enterprise-grade audio caching.

Agents don't share context. You copy a brief into Claude, paste it into ChatGPT, drop the same .md into Cursor — and watch them drift. OzBrain solves this with a shared brain that every agent reads and writes. Build a workflow that keeps all your agents synchronized.

Munder Difflin wraps your existing CLI agents into an autonomous office of clones that share context, hand off tasks, and work around the clock. Build a production workflow that turns Claude Code, Codex, and Copilot into a coordinated team.

Frontier models are generic. Your agents need domain-specific intelligence. Prime Intellect's RL training stack lets you turn any task into a reinforcement learning environment, train custom models on 2,500+ community environments, and deploy with 1-click inference.

SOC analysts face 10,000+ alerts daily with 94% false positive rates. Autonomous correlation agents map alerts to MITRE ATT&CK techniques in real-time, cluster related events into incidents, and reduce analyst workload by 89% while catching genuine threats 3.2x faster.

Manual A/B testing is dead. Autonomous experimentation agents now run multi-variant tests, detect statistical significance, and auto-promote winners without human bottlenecks — cutting experiment cycle time from weeks to hours.