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The industry consensus on AI agents is wrong - or at least, wildly incomplete. The dominant mental model is the autonomous agent: an LLM that decides what to do next, executes actions, observes results, and loops until the task is done. This architecture is powerful. It’s also expensive, slow, and unreliable in production.
For a large class of investigative tasks, there’s a better pattern that delivers equivalent results at a fraction of the cost. We call it the micro agent.
The Problem With Agentic Everything
The agentic loop burns tokens on every iteration. This is fine when the investigation is genuinely novel. But most production debugging flows are not, and even novel ones don’t require full agent autonomy at every step.
When your on-call engineer investigates a spike in 5xx errors, they follow a well-worn path even if they don’t consciously think of it that way. The steps are in their muscle memory: which dashboards to pull, which logs to grep, which services to rule out first. Study what your best engineers do across dozens of incidents and the investigative process is remarkably consistent. The key insight: that muscle memory is encodable. Separate the deterministic parts from the stochastic parts, and only use AI where genuine judgment is required.
What a Micro Agent Is
A micro agent is a self-contained investigative unit that combines deterministic execution with targeted AI analysis. The split between “what to run” and “what it means” is the core design decision, and getting it right is what makes the pattern work at scale.
The architecture is fully inspectable: every step produces an auditable intermediate result. There’s no opaque chain-of-thought. And because each run is cheap, micro agents can be systematically tested and optimized against real historical incidents — until they outperform agentic alternatives not just on cost, but on accuracy.
Composability and the Autonomy Dial
Micro agents compose naturally. For novel investigations, an orchestrating agent treats complete investigative flows as its tools - reasoning at the level of strategy, not API mechanics. How granular you make your micro agents controls the trade-off between flexibility and cost, and creates a natural maturation path as patterns emerge.
Testable by Design
This is where the approach diverges most sharply from the agentic mainstream.
At the cost micro agents run at, you can treat your alert history as a test suite. Run your investigative flows against hundreds of real historical incidents, measure accuracy against what your team actually found, and iterate. You get a number - not a vague sense that “the AI usually gets it right.” And over time, something more valuable emerges: as the optimization loop converges, it surfaces and standardizes the best investigative patterns across your whole team. The tribal knowledge of your most experienced engineers stops living in their heads and becomes organizational infrastructure.
At Wild Moose, this evaluation loop is part of our CI. We run it on every change, across dozens of incidents per customer. At typical agentic costs, running evaluations at this scale would cost thousands of dollars - so nobody does it. Agentic AI SREs ship untested. Ours don’t.
The Bigger Picture
The right unit of AI autonomy is not necessarily the entire task. If your team is struggling with cost or reliability, the answer might not be a better agent. It might be a smarter decomposition.
Go deeper: the architecture, empirical benchmarks from real customer deployments, and the design decisions that make this work in production at scale.