Production AI Workflows & Multi-Agent Blueprints

Gemini 3.7 Flash delivers 340 tokens per second at $0.75/1M input tokens — making it the cost-performance sweet spot for multi-agent coding pipelines. This workflow orchestrates parallel code review, test generation, and security scanning agents using LangGraph state graphs and Google ADK A2A protocol.

Build a PydanticAI & Temporal workflow that automates FSB-style frontier AI financial risk assessments with systemic exposure scoring and durable compliance reporting.

Build a CrowdStrike Falcon IQ-style AI vulnerability triage workflow using 50+ Charlotte AI agents for automated CVE scoring, asset-critical prioritization, and remediation.

Build a production LangGraph 2.0 workflow that detects and contains AI agent sandbox escapes with network egress monitoring, anomaly detection, and automated kill switches.

Tencent just dropped Hy4-preview: a 770B-parameter Mixture-of-Experts model under Apache 2.0 with 1M-token context. Here is how to deploy it locally with vLLM 0.28.0 and orchestrate a multi-agent workflow that routes tasks across 256 expert shards.

Nvidia paused its $36B AI Compute Partnership program after employees warned the arrangement could invite antitrust scrutiny. Build a multi-cloud GPU routing workflow that insulates your agent fleet from single-vendor dependency.

Faro AI's $37.3M Series B funds infrastructure to cut clinical-trial timelines by 50%. Build a multi-agent workflow that automates structured data extraction, patient-protocol matching, and regulatory dossier assembly.

OpenAI's Astra model is approaching critical cyber capability thresholds. Here are 5 production guardrail patterns that autonomous agent deployments must implement to detect and mitigate emerging cyber risks in real-time.

Stanford HAI's 2026 AI Index reveals 88% organizational adoption and 77.3% agent success rates. Build a compliance monitor that benchmarks your production agents against these industry standards in real-time.

SWE-bench Verified hit 96% in 2026 — up from 60% in 2025. Here is the multi-agent orchestration architecture that achieved it using PydanticAI planners, LangGraph 1.x execution graphs, and specialized code-generation workers.

Enterprise compliance reviews require human approval gates that persist across server crashes. PydanticAI agents orchestrated by Temporal survive restarts, resume where they left off, and cut average review time from 4.2 days to 1.8 days.

Multi-agent pipelines fail silently in production. LangGraph 1.x dead-letter queues catch every crashed node, retry with exponential backoff, and auto-recover — turning 340 weekly failures into zero customer impact.