No think-pieces, no predictions. Implementation detail from agents running in production right now — what worked, what broke, and the numbers behind both.
Most things sold as 'AI agents' are a prompt in a loop. Here is the real anatomy — model, tools, context, and control loop — and how to tell whether you need an agent at all.
Vector search on 512-token chunks is a demo, not a system. Chunking, hybrid retrieval, reranking, metadata filtering and the retrieval evals that stop you flying blind.
Vibes-based testing is why AI projects stall at 'promising demo'. A practical guide to building eval sets, choosing metrics, using LLM-as-judge without fooling yourself, and catching regressions.
Hallucination isn't a mysterious model flaw — it's a system design failure with known causes and known fixes. Retrieval grounding, decision boundaries, confidence thresholds and citation validation.
Prompt caching, model routing, batching and context discipline. Four levers that reliably take an order of magnitude off an AI bill — with the mechanics of why caching silently fails.
Modern models don't need clever phrasing. They need the right information, in the right order, with nothing else in the window. What actually goes in a production context — and what to cut.
A sequenced plan for automating an e-commerce support queue — which ticket types to take first, where the deflection ceiling really is, and the metrics that stop you optimising the wrong thing.
If your agent reads untrusted content and can take actions, you have an injection surface. Why filtering doesn't work, and the architectural controls that do.
Benchmarks won't pick your model. A decision framework based on task shape, routing per step, reasoning effort, and the migration costs nobody prices in.
Most AI business cases are built on hours saved that never left the payroll. A method for measuring what actually changed — baselines, counterfactuals, and the four categories of real return.
Orchestrators, subagents and parallel fan-out are genuinely powerful and routinely misapplied. How to tell the difference, and how to build one that doesn't cost 5× for the same result.
The demo worked. Nine months later nothing is in production. Seven structural reasons AI projects die between staging and live — and what the teams that ship do differently.
A 30-minute audit call. We map your workflows and name the three highest-leverage agents, with what each is worth.