Ever had your system fall over because everyone decided to use it at the same time? Yeah, that’s not a great feeling. Even worse when those users are paying customers who’ve scheduled important AI agents to run at 9 AM sharp.
Summary
A no-code agent platform ran into reliability issues when users started triggering their workflows all at once. Instead of scaling compute or rewriting backend services, the team added a lightweight event control plane. This gave them control over retries, rate limits, and bursts with minimal code changes.
The problem: all the agents run at 9AM
The platform let users schedule agents to automate things like daily summaries, weekly reports, and outbound emails. Most users wanted these tasks to run at the top of the hour. That meant heavy traffic spikes at 9 AM, noon, and 5 PM.
The backend started to struggle. Tasks dropped during peak load. Retry logic backed up. Reliability dropped when users needed it most. Product teams held back features because they weren’t confident the system could handle the load.
What they tried (and why it wasn’t enough)
The engineering team tried a number of fixes:
- Queues helped absorb traffic but backed up quickly
- Manual retry logic was scattered across services
- Scheduled staggering didn’t align with real user demand
- Load shedding dropped lower priority tasks but frustrated users
- Overprovisioning compute became expensive fast
None of these addressed the core issue. The system had no reliable way to coordinate bursts or apply backpressure cleanly.
The turning point: using events to regain control
Rather than keep patching things, the team introduced Sailhouse as an event control plane between agents and backend services.
Here’s how the architecture changed:
- Agent triggers emitted events instead of making direct calls
- Sailhouse buffered spikes locally and applied per-subscriber rate limits
- Retry policies were set declaratively, not in code
- Failed deliveries triggered fallback alerts automatically
The backend stayed the same; only the communication model changed.
Why events worked better than more queues
Queues could handle bursts but offered limited control. Sailhouse let the team:
- Rate-limit specific consumers without deploying code
- Centralise retries instead of duplicating them across services
- See pending, successful, and failed events in one place
The system moved from scattered logic and guesswork to a clear, observable flow.
What changed
- 4x increase in agent volume, with no infrastructure increase
- 37% fewer task failures during peak times
- Fewer on-call issues tied to spike-related outages
- Product teams could ship features that used scheduled agents without worrying about platform stability
Why it matters
AI agents often look simple at first. But when usage ramps up, coordination becomes the real problem. Bursty workloads break systems that rely on synchronous calls and hardcoded retries.
This team didn’t need to scale their backend or rebuild their platform. They just changed how services talked to each other. That was enough to turn failure-prone spike handling into a non-issue.