All field notes

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.

RAG was supposed to stop hallucination. It reduces it—if built well. Why retrieval-augmented systems still make things up, and the fixes that actually work.

Should you call a public LLM API or run a private model? The real trade-offs in data control, cost, latency, and capability—and how to decide.

Most AI projects don't fail on the model; they fail on the data. The readiness gaps—quality, access, structure, governance—that stall AI, and how to fix them.

Most AI chatbots frustrate users and erode trust. The reasons—hallucination, no escalation, wrong scope—and how to build one people actually rely on.

You can't trust what you can't measure. How LLM evaluation—test sets, metrics, human review, monitoring—turns 'it seems to work' into evidence.

Prompt engineering is table stakes. Context engineering—what data, memory, and structure you give the model—is what actually makes AI systems reliable.

AI governance is what lets enterprises say yes to AI safely. The policies, controls, and oversight that manage risk without freezing every project.

Your team is already using AI—often with company data, on tools nobody approved. The risks of shadow AI, and how to bring it into the light safely.

The biggest model isn't always the right one. How to choose an AI model by matching capability, cost, latency, and data needs to your actual use case.

Fine-tuning and RAG are not competitors—they solve different problems. When you need to change behavior, when you need current data, and when to use both.

A champion needs a business case a CFO will accept. How to calculate AI project ROI honestly—conservative math, transparent assumptions, and a real payback period.

AI vendor pitches all sound the same. A six-criteria framework—production proof, security, evaluation, ownership—to tell real capability from confident hype.

Before AI touches production data, run this security checklist—data handling, access, prompt and model security, and the vendor questions to ask first.

How long does AI implementation take? The real phases—discovery, build, pilot, production, adoption—what drives the timeline, and how to avoid the usual delays.

The build price is a fraction of the real cost. The full TCO of an AI system—inference, data, monitoring, maintenance—so your budget doesn't blow up later.

A vague AI RFP gets vague proposals. How to write one that surfaces real capability—outcome-first, with the reliability and security questions that matter.