
AI Agent Orchestration at Scale: How to Size a System for 10,000 Concurrent Workflows
Size an AI agent fleet with the math: Little's law for concurrency, token-per-minute limits for LLM calls, backpressure, and the autoscaling signals to watch.

Size an AI agent fleet with the math: Little's law for concurrency, token-per-minute limits for LLM calls, backpressure, and the autoscaling signals to watch.

Dapr 1.19 makes workflows faster with stateful worker streams and an opt-in fast path that cuts median step latency from 553 ms to 153 ms, adds detached and cross-app workflows with access policies, lets the Scheduler take over actor placement, and introduces alpha Binary Store, search, vector, and MCP server APIs.

A new open-source integration runs Goose agents inside Dapr Workflows, so they survive crashes, skip completed turns, and resume with their context intact.

Build visually in n8n. Execute durably with Dapr. Completed nodes stay completed, even when the worker crashes.

A practical comparison of 17 AI agent frameworks: programming model, languages, multi-agent support, and what each one keeps when a run crashes.

Previously reserved for paying customers, features now provided for free include multi-cluster management, best practice advisor, and automatic Dapr rollouts.

Multi-cluster management, automated upgrades, 150+ metrics, and best-practice advisories are now free for every Dapr team. No credit card, no trial period.

How Temporal, AWS Step Functions, Cloudflare, DBOS, Azure and Diagrid price durable execution, what each meter counts, and what AI agents do to the bill.

Gateways, identity vendors, cloud platforms, and durable runtimes each cover one piece of agent security. Learn the 8 questions that expose the gaps in an AI agent security stack.

Before an AI agent reaches production in a bank, risk review moves past model performance. Learn the 9 execution-control questions and how to answer each one.

Take a LangGraph agent from demo to production. 12 checks covering checkpointers, persistence, retries, interrupts, identity, tracing and rollout.

Run Diagrid Catalyst in our cloud, on dedicated infrastructure, in your own AWS or Azure account, or fully air-gapped. Compare all five deployment models.

Diagrid's new Spring AI integration brings durable execution to your agents. Add one dependency, crash the process mid-run, and watch the agent finish the job.

Agent frameworks make three different persistence promises. Learn why only a runtime below the framework can detect failure and finish a Spring AI agent's run.

Add durable execution to LangGraph, CrewAI, ADK, or Strands agents with one package and a wrapper. See how Diagrid Catalyst keeps your framework intact.

Hidden flags, durable Go agents, and two talks. Cassie Coyle and Sam Kendziora recap GopherCon 2026 — and the CTF challenge is still open until August 28.

Saving state is the easy half. Resuming from the failing step is the half that decides whether your agent reaches production.

Long-running AI agents re-spend tokens on every completed step after a failure. Agentic durable execution ends the rework by resuming at the failure point instead of restarting from the top.