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 Cost Curve Breaks at Scale: Why Partitioned Small Language Models Drive Real Enterprise ROI
Enterprises achieve sustainable AI economics not by scaling model size, but by strategically partitioning workloads to deploy small language models for high-volume tasks while reserving larger models for complex reasoning.
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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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