Running Agents

Execution modes, local limits, and scaling.

Three ways to run

Single agent — run one agent for a focused task. The agent gets its context cascade, executes autonomously, and writes results to GitHub and memory. This is the building block.

squads run research/analyst
squads run intelligence --task "Scan competitor pricing changes"

Squad conversation — run an entire squad as a coordinated team. The lead briefs first, workers execute in parallel, the lead reviews outputs, and the cycle repeats until the team converges on a result. This is where multi-agent synergy happens.

squads run research --parallel

Autonomous dispatch — let Squads decide what to run, when, and in what order. Autopilot reads priorities and feedback, respects phase ordering, and manages budget constraints. This is the hands-off mode for continuous operations.

squads autopilot --interval 30 --budget 50

Local execution

Squads runs locally by default — your machine, your API keys, your control. There's no cloud dependency for core functionality. Each agent execution spawns a CLI process (claude, gemini, etc.) that runs until completion. Your data never leaves your machine unless the agent explicitly pushes to GitHub or another service you've configured.

Local limits

Parallel squads	Machine
2–3	8 GB RAM, 4 cores (laptop)
4–6	16 GB RAM, 8 cores (workstation)
8–12	32 GB+ RAM, 10+ cores (M-series Mac / desktop)

Actual capacity depends on your CPU, memory, and which providers you use. squads autopilot --max-parallel 3 controls concurrent executions. Monitor with squads sessions.

Cloud scaling

Local execution works well for individuals and small teams, but it has natural limits — your machine needs to stay running, parallel execution is bounded by hardware, and there's no shared visibility across team members. When you're ready to scale autonomous operations across teams, cloud execution runs the same agents, same memory, same commands — but on managed infrastructure instead of your laptop.