Building AI Agents That Actually Work in Enterprise Settings
By Corvic AI Engineering
The AI agent hype cycle is in full swing, but the reality in enterprise settings is sobering: most agents fail within weeks of deployment. They hallucinate, lose context, exceed their authority, or simply can't handle the complexity of real enterprise data.
We've spent two years building agents that actually work in production. Here's what we've learned:
1. Ground everything in data. Agents must reason over your actual data, not generate plausible-sounding responses from training data.
2. Constrain intelligently. The best agents have clear boundaries — they know what they can and can't do.
3. Evaluate continuously. Agent performance degrades over time without ongoing evaluation against ground truth.
4. Compose, don't monolith. Enterprise tasks are better served by compositions of specialized agents than by one do-everything agent.