An LLM answers. An agent acts. The distinction sounds small — it reshapes everything about how software gets built, debugged, and deployed.
From completion to composition
The first generation of LLM products were chat interfaces: human asks, model answers. The second generation is agentic: the model decides what to do next, calls tools, checks its own output, and loops. This changes the contract — suddenly the model is not a peripheral but an executor with planning authority.
What makes an agent reliable
- Grounding: verifiable state the agent can read (file system, database, APIs)
- Affordance: well-typed tools with narrow, composable contracts
- Memory: structured context windows, not ever-growing conversation logs
- Recovery: the ability to detect and unwind failed subtasks
The production reality check
Most agent demos are theater. Production agents that actually ship work in narrow domains with high-quality tools and deterministic fallback paths. The best-performing systems we’ve audited in enterprise deployments share a pattern: small, single-responsibility agents orchestrated by a deterministic workflow — not a single “do everything” agent.
Where this is heading
Tool-using agents with strong typing and sandboxed execution will replace large classes of RPA and legacy automation within three years. The bottleneck is not model capability — it’s tooling, evaluation, and observability. The first company to build the “Datadog for agents” becomes important very quickly.