Agentic AI in the Enterprise: Where It Works and Where It Doesn't

Agentic AI is the most overhyped and underappreciated development in enterprise software simultaneously. Overhyped in its near-term breadth. Underappreciated in the depth of value it will create in specific, well-scoped applications.

Where It Works

The use cases that are succeeding in early enterprise deployment share a common profile: bounded scope, well-defined success criteria, human review before consequential actions, and strong observability.

Code review and generation. Document summarisation and extraction. Structured data enrichment. Research synthesis across corpora. These aren't flashy demos, but they're delivering real ROI.

Where It Fails

Agents fail in open-ended, high-stakes environments without adequate guardrails. An agent that can take arbitrary actions across enterprise systems is an incident waiting to happen. The failure modes aren't always obvious — subtle misalignments in goal interpretation compound over multi-step tasks in ways that are genuinely hard to anticipate.

The Infrastructure Gap

Most enterprise environments aren't ready for agents not because the models aren't capable, but because the surrounding infrastructure doesn't exist. You need robust tool permission systems. You need interrupt mechanisms. You need audit logs that capture every action and its rationale. You need clear escalation paths when an agent hits a decision it shouldn't make autonomously.

The Practical Path

Start narrow. Define a specific task with clear boundaries. Add human-in-the-loop checkpoints at high-consequence decision points. Build your observability layer before you need it. Then expand scope incrementally as you develop trust in the system's behaviour. Agents are not a product decision. They're an infrastructure investment.