What actually breaks when AI leaves the demo and meets real operations — and how to keep it running.
Prompts get edited in a console by whoever is closest to the problem, with no diff, no review and no way back. Treat prompt edits like the deploys they are.
Agents read emails, tickets, PDFs and web pages. Any of that text can carry instructions. Why injection is a blast-radius problem, not a prompt problem.
Timeouts, retries, and parallel workers make agents repeat real actions. How to make every side effect safe to run twice.
Model upgrades and prompt tweaks are guesses without a fixed set of scored examples. How to build the regression set that makes every change safe to ship.
Agents give bad answers because the documents are wrong, stale, or missing — not because the index needs tuning. What to fix before you touch retrieval.
Most failed AI agents are not short on intelligence — they are short on access. How to scope an agent's read, write, and permission boundaries.
Three layers of automation solve three different problems. Choosing the wrong one is the most expensive mistake in an AI programme — and the easiest to avoid.
Behind every agent that survives contact with real volume there is a queue, a schema, a check, an audit trail and an owner — not a conversation window. The anatomy of a deployed agent.
Prompting has the shortest shelf life of any AI skill. Enablement should teach task decomposition, evaluation, and knowing what not to automate instead.
Human review is where AI workflows quietly fail — too much approval and the automation saves nothing, too little and errors ship. How to place it.
Teams spend weeks choosing a model and no time deciding what the automation is allowed to spend. The cost ceiling is the design decision that determines whether an AI workflow survives contact with real volume.
Most teams deploying AI have no idea whether it is doing its job. Four metrics — attempted, succeeded, escalated, cost-per-task — turn a hopeful deployment into an observable one.
The gap between a working AI demo and a working AI system is not intelligence — it is operations. Here is what actually kills agents in production, and what to build instead.
Most automation programmes fail because they start with the hardest or the most visible process. A short, practical way to score candidate workflows and pick the one that will actually ship.