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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Insights
The AI Consulting Mandate: From Benchmarking to Co-Development
Enterprise AI procurement is shifting from vendor benchmarking to structured co-development partnerships that align technical delivery with internal operating design.
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Governance
Stop Treating AI Governance as a Checklist. Embed Risk Controls in the Runtime.
Enterprise AI governance must shift from static compliance checklists to dynamic, architecture-embedded risk controls that scale alongside autonomous workflows.
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Retrieval
The RAG Architecture That Actually Scales: Dropping Model Chasing for Measured Retrieval
The winning AI implementations now come from disciplined architecture and measurement, not from chasing the newest model.
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Retrieval
Architecture-Centric Deployment Outperforms Model Swaps for Enterprise AI Operating Leverage
The winning AI implementations now come from disciplined architecture and measurement, not from chasing the newest model.
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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.
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Retrieval
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.
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Agents
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.
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Governance
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.
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Retrieval
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.
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MLOps
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.
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Governance
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.
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Strategy
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.
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Governance
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.
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Retrieval
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.
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