14 published articles across 4 topic clusters — AI readiness, agents and automation, and enterprise knowledge. Every article passes editorial review before it appears here; new briefs are added as they are approved.
Showing 14 of 14 published articles
The concrete controls an AI feature needs before it touches real users and real data — access, data handling, logging, and abuse resistance.
A practical evaluation method: build a test set from your real workload, score models on your tasks, and price the winner at your volumes.
A structured way to diagnose a stalled AI initiative: separate data, model, integration, and governance failures before deciding what to fix.
How to restart a failed AI initiative: reset scope to one provable workflow, fix the data path first, and put ownership in writing before rebuilding.
What an AI agent actually is, where it beats a chatbot or a script, and which business workflows are realistic starting points.
The mechanics of agent-driven workflow automation: decomposition, tool integration, state, and the human checkpoints that make it safe.
When splitting work across specialized agents beats one capable agent — and when the extra coordination just adds failure modes.
What actually drives AI agent cost: integrations, evaluation, and guardrails — plus the operating costs that outlast the build.
A staging strategy for agents: sandboxed tools, recorded scenarios, adversarial cases, and a supervised shadow period before real access.
The recurring production failure patterns for AI agents — and the design decisions that prevent each one.
How RAG actually works in production: document pipelines, chunking, retrieval quality, and grounding — the parts that determine whether answers are right.
A decision guide: RAG for knowledge that changes, fine-tuning for behavior and format — and why most business assistants need RAG first.
