AI Agents Are Everywhere, Most Aren't Production-Ready
Every vendor is shipping an "AI agent." Most are thin wrappers around a chat API with a fancy UI. Enterprise teams need agents that integrate with existing systems, respect access controls, and produce auditable outcomes.
Start With One Workflow, Not a Platform
Pick a single high-friction workflow: ticket triage, invoice matching, lead qualification, or internal knowledge search. Measure baseline time and error rate before building anything.
Architecture That Survives Compliance Reviews
Non-negotiables for enterprise deployments:
- Role-based access to tools and data sources
- Human approval gates for write actions
- Full request/response logging with retention policies
- Cost caps and token budgets per user/team
- Fallback paths when the model fails or times out
Tool Design Matters More Than Model Choice
Agents fail when tools are vague. Each tool should do one thing, return structured JSON, and validate inputs before side effects. GPT-4o, Claude, and Gemini all work, bad tool schemas break all of them equally.
Measuring Success
Track containment rate (resolved without human), average handling time, escalation rate, and user override frequency. If overrides stay above 30% after four weeks, the workflow isn't ready to expand.
Conclusion
AI agents deliver ROI when scoped tightly, integrated deeply, and governed seriously. Book a consultation to map agent opportunities in your stack.
