Guides
Everything on this site reduces to one claim: you can already get one AI agent to do a task — the leverage is in running a pod of them at once, without becoming the queue. These guides work that claim through: the definitions, the workflow, the collision problem, the math, and the honest "do you even need this yet."
What Is AI Agent Orchestration?
The definition that matters: many agents, one point of control — and the five problems every real orchestration layer has to solve.
What Is an Orchestrator Agent? (vs. a Single AI Agent)
The orchestrator doesn't do tasks — it directs the agents that do. The job description, the side-by-side, and why the difference matters to a buyer.
How to Run Multiple AI Agents in Parallel
The six-step workflow: split the backlog, isolate every agent, launch the pod, brief like a hire, turn on autopilot, review diffs not vibes.
How Do You Keep Parallel AI Agents From Stepping on Each Other?
The collision failure modes, why one git worktree per agent is the right isolation cell, and what watches the lanes on top of it.
Agent Orchestration vs. No-Code Automation Platforms
Zapier-style tools run steps you predefined; orchestration runs agents on work you describe. Which tool fits which half of your workload.
What Does Running AI Work One Task at a Time Actually Cost?
Three cost layers — the wall-clock queue, the dispatcher tax, and the work that never starts — with illustrative math you can re-run on your own numbers.
7 Mistakes Founders Make When Orchestrating AI Agents
Shared workspaces, overlapping tasks, search-box briefs, trusted summaries, human dispatching, nursed agents, premature scale — ranked, with fixes.
Do I Need Agent Orchestration — or Just One Good Agent?
The straight answer to the most-asked pre-alpha question, including the cases where one agent (or an always-on cloud agent) genuinely covers you.
The tool behind all of it
Orca — an orchestrator for Claude Code. A pod of agents, one terminal, autopilot in the gaps. Private alpha, opening soon.
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