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Life, tech, and everything in-between.
I Read the Code
The planner, coder, judge split is a good harness: an expensive model plans, a cheap one writes, the expensive one judges, with the budget spent where judgment lives. But an LLM judge is a filter, not an alibi; it catches the cheap model's misses, not whether the plan solved your problem or whether the edge cases the models quietly agreed on match the ones your users will find. The one step that does not delegate is the last: somebody opens the diff and reads it.
Stop Building Agent Demos. Start Building Agentic Software.
Agentic AI is in its demo phase, where a chain of prompts looks magical in a notebook but falls apart in production. The discipline that separates the two is knowing where reasoning belongs to the model and where reliability belongs to ordinary code.
What 'AI-Native' Actually Means
A story about a team building an AI-native product. They design the UI first and the model breaks it. They delete the UI and the users break. They hand the agent their old API, then make people learn a control panel. Then they find where the interface actually belongs.
The Agentic Future and the Token Economy
The agentic future is now mainstream consensus, and tokens have become the unit of account for the intelligence economy. But tokens are a unit of labor, not money; the real money between agents is stablecoins; and the most actionable move is building software agents can consume.
SynthID: How Google Watermarks AI Content
Google has watermarked over 10 billion pieces of AI content with SynthID. The text version is open source, the algorithm is genuinely clever, the detector portal is free for journalists. It is also not a silver bullet, and the academic literature has been clear about why.
Choosing the Right Agentic Pattern: A Decision Framework
Thirty-three articles into this series, the question becomes how to pick among the patterns rather than how to build any one of them. A small set of decision trees and a simplicity test cover most cases.
Semantic vs Agentic RAG
Semantic RAG costs roughly $0.001 to $0.01 per query. Agentic RAG costs $0.02 to $0.10 and can be 5 to 50 times slower. The accuracy delta on multi-hop questions is also real. A short guide to picking the right default.
Agentic RAG
Semantic RAG is a fixed pipeline. Agentic RAG hands retrieval control to the model: when to retrieve, what to query, how to combine, when to stop. A short guide to the patterns that matter and how to ship them.
MCP and A2A: Protocol Standards for LLM Agents
For a decade, every tool integration and every inter-agent communication was bespoke. MCP standardizes the data side: any client connects to any tool server. A2A standardizes the agent side: agents discover and call each other across organizations.
Scaling and Cost Optimization for LLM Agentic Systems
Multi-agent architectures amplify both capability and error. Google DeepMind measured the amplification across 180 configurations. Anthropic reports 90 percent cost reductions from a planner-plus-workers split. Both results inform how to scale production agents.
Semantic RAG
Semantic RAG is a fixed pipeline: chunk, embed, search, generate. Tuned well, it handles most production lookup workloads at a cost the alternatives cannot match. A short walkthrough of the pieces and the upgrades that matter.
Harness Design: Planner, Generator, Evaluator for Production LLM Agents
A harness is the set of components around a language model that turn a research prototype into a production system. Anthropic's three-agent architecture (planner, generator, evaluator) mirrors the GAN discriminator-generator dynamic and survives long-running applications.