Scaling from 1 to 1,000 Agents: Lessons Learned
What we learned building infrastructure that scales from a single agent to thousands running concurrently.
When we started building Padiso, our first customer ran a single agent. Today, our largest deployment runs over a thousand concurrent agents. Here's what we learned along the way.
Lesson 1: Isolation Is Non-Negotiable
Every agent must run in complete isolation. Not just for security — for reliability. One misbehaving agent should never affect another. We use per-agent containers with strict resource limits, network isolation, and separate credential stores.
Lesson 2: Not All Agents Are Equal
Some agents process one task per hour. Others handle hundreds per minute. A fixed resource allocation model doesn't work. We moved to dynamic scaling — each agent gets resources proportional to its workload, with automatic scale-up and scale-down.
Lesson 3: Integration Rate Limits Are the Real Bottleneck
Your agent might be fast, but the Slack API has rate limits. The GitHub API has rate limits. Every external service has limits. We built a centralized rate limit manager that coordinates across all agents, preventing any single agent from exhausting shared quotas.
Lesson 4: Observability at Scale Requires Structure
With 1,000 agents, you can't just read logs. We built structured telemetry from day one — every task, API call, and state change is a structured event. This lets us build dashboards, alerts, and debugging tools that actually work at scale.
Lesson 5: Graceful Degradation Over Hard Failures
Agents should degrade gracefully when dependencies fail. If Slack is down, queue the messages. If the database is slow, reduce batch sizes. We built circuit breakers and fallback strategies into the platform so agents stay resilient.
What's Next
We're working on multi-agent coordination — letting agents communicate and collaborate with each other. Stay tuned.