How teams keep AI cost and behavior under control across every coding tool they use and every AI feature in their product.
11 posts
AIWatcher Local reads your coding sessions. Here's the honest answer to why it's open source, what that commitment actually covers, and how to verify it yourself.
A control loop for AI work supervises AI before, during, and after it runs: on developer laptops and inside your product. Here's how the five steps work.
AI agents fail by running, not crashing. Four named failure modes (Retry Storms, Context Starvation, Prompt Bloat, Cost-per-Success Drift) and the telemetry signals that catch them.
AI agents fail differently than traditional services — they don't crash, they keep working expensively or in loops. Here's how to monitor what actually matters: cost, behavior, patterns, and outcomes.
AI spend has crossed from R&D to cost of goods sold. The accounting choice changes gross margin, valuation multiples, and the unit economics conversation with your board. Here's the framework.
A technical guide to building per-customer cost attribution for AI features — and why this is suddenly a board-level conversation in 2026.
A practical guide to catching runaway spend in real time across Claude Code, Cursor, Codex CLI, and direct API usage — before the bill arrives.
Five real options for engineering leaders managing AI tool spend across multiple developers — what each one solves, where each one breaks, and what to use when.
The first era of AI observability watched the model call. The next connects AI activity to cost, context, risk, and outcomes — from token counts to cost per useful outcome.
LLM observability describes model calls. Agents do work, locally and in production. The next layer ties cost to outcomes and evidence, and closes the loop by acting on what it finds.
How a meeting notes pipeline ran unattended for 10 days, generated 31,762 session files, and what it taught us about the visibility gap in agentic AI.