So we had a bug in one of our AI Agents the other day.
It was still telling people to come to SaaStr AI London 2025 on Dec 1-2.
Of course, it already happened. 😅
The issue wasn’t fixing it. The issue was — which agent was saying this?!
We have 20+ AI Agents running across our operations now. Marketing agents. Support agents. Content agents. Scheduling agents. They’re all out there, autonomously doing work, talking to prospects and customers, sending emails, updating content.
And it really took a while to debug.
Not because the fix was hard. But because finding the problem across 20+ autonomous agents — each doing their own thing, each with their own context, each making their own decisions — is genuinely difficult.
This Is Going to Be One of the Defining Challenges of 2026-2027: How Many AI Agents Can We Really Manage?
Think about where we’re headed:
- 2026: Maybe you have 5-20 AI agents if you’re aggressive about adoption
- 2027: Could easily be 50-100 agents for a mid-sized SaaS company
- 2028: 1,000+ agents isn’t crazy. Every workflow, every micro-process, every customer touchpoint
Now imagine our little London date bug… but across 200 or more AI agents.
Impossible. Or at least, a huge headache to debug.
You simply cannot manually audit 200 autonomous agents making thousands of decisions per day. You can’t spot-check your way to quality control. You can’t have a human reviewing every output.
We’re Going to Need Master Agents
One next big unlock in AI infrastructure is Master Agents — AI systems whose sole job is managing, monitoring, and debugging other AI agents.
Think of it like this:
- Master Agents that detect when a downstream agent is giving outdated information
- Master Agents that notice when an agent’s outputs drift from your brand voice
- Master Agents that flag when one agent contradicts another
- Master Agents that can trace a customer complaint back to the specific agent interaction that caused it
Essentially, AI DevOps for your AI workforce.
This already exists, to some extent. Replit, Cursor and others have subagents and senior agents. But that’s all within the same application, more or less. That makes it easier.
The Observability Problem is Real
In traditional software, we solved this with logging, monitoring, and observability tools. Datadog, New Relic, etc.
But AI agents are different:
- Their “bugs” aren’t always errors — sometimes they’re just wrong or outdated or slightly off
- They make judgment calls, not deterministic outputs
- They interact with each other in ways that create emergent behaviors
- The volume of their outputs makes human review impossible
We need new tooling. We need new frameworks. We need Master Agents.
As Deploying AI Agents Gets Easier, Managing Them Will Have To, Too
Deploying AI agents is getting easier every month.
Managing AI agents at scale? We’re still in the very early innings.
Our little London date bug was a wake-up call. At 20 agents, debugging is annoying. At 200 agents, it’s unmanageable. At 1,000 agents, you either have Master Agents handling it… or you have chaos.
The future isn’t just about having more AI agents.
It’s about having the AI infrastructure to actually manage them.
