Think Miniml.
Practical writing on shipping AI into production — agents, retrieval, governance, and strategy.
Writing on shipping AI into production.
What are agentic workflows? Patterns, use cases & examples
Our clearest primer on agentic patterns — what they are, where they fit, and how to tell a genuinely agentic workflow from automation wearing the label. The place to start if agents are on your roadmap.
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Agents
Most Enterprise AI Agents Fail for the Same Reason: They Skip Operating Design
Enterprise agents create value only when paired with process control, evaluation, and clear economic boundaries.
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Strategy
Enterprise AI Strategy Has Moved From Pilots to Workflow Economics
The right enterprise AI strategy is not broader experimentation; it is tighter selection of workflows where reliability changes unit economics.
Read more →RAG is dead? Long-context windows vs. retrieval
Bigger context windows were supposed to kill retrieval. They didn’t — here’s where each one wins, and why production systems still reach for RAG.
Read more →Agents in production: LangGraph vs CrewAI vs AutoGen vs ADK
A practitioner’s comparison of the major agent frameworks on what matters once you leave the demo — control, observability, and reliability under load.
Read more →The EU AI Act is live: is your legacy model compliant?
The Act is in force and the obligations are real. What it means for models already in production — and how to tell whether yours is exposed.
Read more →Evaluating RAG: faithfulness, groundedness, answer relevancy
“Looks right” is not a deployment criterion. The metrics — and the harness — that turn a retrieval system into something a team will sign off on.
Read more →LLM observability: tracing, evals, and cost/latency
You can’t operate what you can’t see. The traces, evaluations, and cost signals that keep an LLM system debuggable once it’s carrying real traffic.
Read more →The ‘right to explanation’: auditable AI decisions
When a regulator or a customer asks why the model decided what it did, “the model said so” isn’t an answer. How to build decisions you can defend.
Read more →Model collapse: why synthetic data needs human verification
Train on your own model’s output long enough and quality quietly decays. Why synthetic data helps — and where a human still has to stay in the loop.
Read more →Hybrid architecture: keep PII on-prem, compute in the cloud
You don’t have to choose between data control and frontier models. The architecture that keeps sensitive data in your perimeter while still using cloud compute.
Read more →RAG vs fine-tuning: when to choose, when to combine
The question is rarely either/or. A decision guide for when retrieval wins, when fine-tuning earns its keep, and when hybrids beat both.
Read more →Want new field notes as we publish them? Book a consultation and we’ll keep you in the loop — no inbox clutter, just the work.
Want this applied to your systems, not just read?
The ideas here come from real engagements. If one of them maps to a problem you’re facing — a pilot that won’t ship, retrieval you can’t trust, an agent that needs guardrails — book 30 minutes with a senior consultant. We’ll tell you where the gaps are and what we’d do next.
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