One agent or many?
A practical guide for AI engineers choosing between single-agent and multi-agent systems, based on task structure, context and coordination costs.
Anup Jadhav
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Latest posts
From Prompt Engineering to Loop Engineering
If you spend any time watching this space, you have watched this vocabulary turn over three times in about a year. Prompt engineering, then context engineering, then harness engineering, and now loop engineering. The usual telling makes that a sequence, each term replacing the last. They are layers, and each
Who Still Understands the Code?
AI coding agents make you dramatically faster. The cost they carry is quieter: a slow erosion of how well you understand the software you are shipping. Here is how I have come to think about that trade, and how I try to stay on the right side of it.
Designing teams for an agentic world
AI coding agents are changing the economics of software development and the shape of engineering organisations. Here is how leaders should rethink build-versus-buy decisions, talent, team structure, platform strategy, and AI governance.
The Frontier of Agent Memory: From Recall to Experience
Part 3 of a 3 part series post about AI Agent memory architecture.
Speculative Decoding
Trading cheap guesses for expensive forward passes
Query, Key, Values
How to think about Q, K, and V vectors in the Attention layer of a Large Language Model
How Modern Agent Memory Architectures Work
Part 2 of a 3 part series post about AI Agent memory architecture.
Why Context Is Not Enough
Part 1 of a 3 part series post about AI Agent memory architecture.
On Durable Objects, Orleans, and prior art for the agentic web
Zak Knill wrote a sharp post this week arguing that LLMs are exposing a gap in our standard cloud-native architecture. The thesis runs like this. HTTP, load balancers, stateless servers, and a central database work fine for request/response, and they fall over when you want to talk to
TIL: Ads in AI chatbots are not just a UX problem
TIL from a paper on ads in AI chatbots that putting adverts inside an AI assistant is not the same as putting ads next to search results. The assistant now has two masters: the user, and the company paying or earning through the ad. And because LLMs respond conversationally, this
Welcome to Middle Loop Engineering
Where engineering rigour goes now that AI writes the code
How fast does it serve? Throughput, latency, and picking the right GPU
Part 2 of 2 on inference engineering for AI engineers.
Fitting LLMs on Self-Hosted GPUs
How much VRAM does your LLM need, and which GPU should you actually rent? A free calculator covering DeepSeek, Llama, Mixtral on H100, B200, A100.
The Harness Is the Product
Where does product quality live in an LLM-based system? A leaked source and a detailed postmortem, both from Anthropic in the last four weeks, make the answer unusually concrete.