[maxbeech]

the thing that actually burns token budget at scale isn't the agent count itself—it's understanding the cost model of orchestrating them. 100 agents running in parallel is fine if they're short-lived queries. but once you start running them on a schedule (hourly checks, overnight batch work), the math changes fast.

each agent run against a real codebase probably spends 20-50k tokens just on context: repo structure, relevant files, recent changes. multiply that by 100 agents running every hour across 10-20 repos, and you're already hitting millions of tokens a day before any actual work happens. add in re-runs for failures or retries, and the cost curve gets steep quickly.

the harder problem is observability. with one agent you can read logs and understand what went wrong. with 100 agents you need aggregation, pattern detection, alerting on the common failure modes. if 3 agents fail silently but identically, was that a real issue or just rate limiting? if 40 agents all timeout at the same step, was it a dependency problem or infrastructure saturation? at scale you're debugging distributions, not individual runs.

also helps to be ruthless about concurrency. the async pattern isn't "run as many as possible at once"—it's "run exactly as many as the API and your budget can support without making the failure modes harder to diagnose." for claude api work that's usually smaller than people expect.