From AI Pilot to Production — The Playbook That Bridges the Gap
Every enterprise has AI pilots. Far fewer have AI in production. The bridge between the two is more about organisational discipline than technical capability. A practitioner playbook.
The Practical State of AI Agents in Mid-2024
The agent conversation has moved from hype to deployment in some categories and remains hype in others. A practitioner snapshot of where agents are actually working and where they are still demos.
LLMOps Maturity — A Practitioner's Maturity Model
Most enterprises are operating LLM workloads on engineering intuition alone. A maturity model helps locate where you are, what to invest in next, and what the next stage actually requires.
LLM Cost Discipline — Engineering Practices That Keep Bills Predictable
Most teams discover LLM cost through the bill. By then, the cost shape is set and hard to change. The engineering practices that keep costs predictable are not exotic, but they have to be in place from the start.
Function Calling — Production Patterns for Enterprise
Function calling turned LLMs from text producers into action takers. The production patterns are constrained: a tight function catalogue, careful permission modelling, robust argument validation, and explicit human checkpoints for irreversible actions.
Multi-Agent Orchestration — Hype Versus Production Reality
Multi-agent frameworks dominate the AI engineering conversation right now. The patterns that actually ship are narrower, more bounded, and more boring than the demos suggest.