AI Agents

AI agents are easy to demo and hard to run reliably. The real failure modes—scope, guardrails, evaluation, oversight—and how to ship agents that hold up.

Guardrails are what make an autonomous agent safe to run. The permission, action, and output controls that bound behavior—and how to design them well.

Not every workflow needs an AI agent—many need plain automation. When rules beat reasoning, when you need an agent, and how to choose without over-engineering.

RPA automates clicks but breaks on change and ambiguity. Where AI agents pick up what RPA can't—and how to modernize automation without ripping it all out.

Agentic AI plans, uses tools, and acts across steps toward a goal—not just answering, but doing. What it is, how it differs from a chatbot, and where it fits.

Multiple specialized agents can outperform one—or multiply the failure points. When multi-agent systems help, when they don't, and how to orchestrate them.

You can't trust what you can't see. The logging, tracing, and evaluation that make AI agents observable—so you catch failures before your customers do.

Autonomous agents introduce new attack surfaces: prompt injection, over-broad permissions, data leakage. The real risks and the controls that contain them.

AI agents are powerful and easy to over-apply. A clear decision guide—based on ambiguity, stakes, and reversibility—for when an agent is right, and when it isn't.

Oversight is what makes an autonomous agent trustworthy. How to design approval gates, escalation, and confidence routing that keep speed and accountability.