blog.md
Articles on AI, engineering, and building products by Subodh Jena.
A new label is going around for building agentic systems as explicit execution graphs. The label is barely real. The shift underneath it is, and the evidence for and against it is more interesting than either side admits.
12 min read
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.
1 min read
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.
11 min read
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.
3 min read
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.
13 min read
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.
14 min read
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.
8 min read
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.
7 min read
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.
8 min read
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.
7 min read
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