9 articles
Quality is the top-cited reason AI agent pilots stall, yet barely half of teams run evaluations at all. What an AI agent evaluation framework actually has to check — trajectory, not just output — and how to build one before you scale.
AI agent memory cost is the budget line most teams discover only after their context window balloons. What naive context injection actually costs, what tiered retrieval saves, and real 2026 pricing to plan against.
Prompt injection is the top-ranked risk in OWASP's 2026 agentic AI report and the reason enterprise agent pilots stall. What the attack looks like, why tool access turns it dangerous, and the containment pattern that works.
AI agent observability cost is the line item most teams forget until an incident forces it. What evals and monitoring actually cost, broken into tooling, compute and human review, with real 2026 figures.
The on-device AI vs cloud AI choice is not a preference, it's an architecture decision with a bill attached. Here's the framework for picking, and the hybrid pattern most production apps end up on.
Generic vendor checklists ask about security and pricing, which every vendor passes. Here are the pass/fail questions a demo can't fake, and the specific risk each one exposes.
The ingest-classify-extract pipeline is not the hard part of document AI. What breaks in production is messy scans, tables, and knowing when to trust an extraction — here's the review-queue pattern that actually works.
Most AI agent pilots stall before production. The causes are architectural, not prompt quality: unbounded autonomy, lost state, no evaluation and no rollback. Here is what to build instead.
A decision framework for RAG vs fine-tuning — what each actually fixes, when the combination is right, and the volume threshold where fine-tuning starts paying for itself.