From tools to teammates: AI agents in action (www.techradar.com)

AI agents are no longer a distant vision but active contributors in enterprises, and this piece argues leaders should treat them like teammates rather than mere tools. The author recommends giving agents clear role definitions (task scope, systems and data access, trigger conditions, escalation paths) and describes concrete agentic use cases—supplier onboarding, support ticket triage, scheduling, workflow observation—to show how autonomy can raise productivity. Crucially, agents can operate solo or as “chained” operatives, so precise directives and interfaces matter to avoid unpredictable behavior. To realize reliable value at scale, organizations must instrument agents with forensic performance metrics, run regular reviews, and iterate via feedback loops. Where performance falls short, reinforcement learning and targeted retraining can steer behavior; where performance exposes gaps, leaders should distinguish “stated truth” (intended workflows) from “observed truth” (actual practice) and adjust roles, tooling, or staffing. The practical implications are governance and operational: access control, monitoring, clear KPIs, and human-in-the-loop oversight will determine whether agents become trusted teammates or brittle automation. In short, agentic AI demands the same management rigor as human employees to deliver safe, scalable ROI.