Monday, 16th March 2026
How coding agents work
As with any tool, understanding how coding agents work under the hood can help you make better decisions about how to apply them.
A coding agent is a piece of software that acts as a harness for an LLM, extending that LLM with additional capabilities that are powered by invisible prompts and implemented as callable tools.
Large Language Models
At the heart of any coding agent is a Large Language Model, or LLM. These have names like GPT-5.4 or Claude Opus 4.6 or Gemini 3.1 Pro or Qwen3.5-35B-A3B. [... 1,187 words]
Coding agents for data analysis. Here's the handout I prepared for my NICAR 2026 workshop "Coding agents for data analysis" - a three hour session aimed at data journalists demonstrating ways that tools like Claude Code and OpenAI Codex can be used to explore, analyze and clean data.
Here's the table of contents:
I ran the workshop using GitHub Codespaces and OpenAI Codex, since it was easy (and inexpensive) to distribute a budget-restricted API key for Codex that attendees could use during the class. Participants ended up burning $23 of Codex tokens.
The exercises all used Python and SQLite and some of them used Datasette.
One highlight of the workshop was when we started running Datasette such that it served static content from a viz/ folder, then had Claude Code start vibe coding new interactive visualizations directly in that folder. Here's a heat map it created for my trees database using Leaflet and Leaflet.heat, source code here.
I designed the handout to also be useful for people who weren't able to attend the session in person. As is usually the case, material aimed at data journalists is equally applicable to anyone else with data to explore.
Tidbit: the software-based camera indicator light in the MacBook Neo runs in the secure exclave¹ part of the chip, so it is almost as secure as the hardware indicator light. What that means in practice is that even a kernel-level exploit would not be able to turn on the camera without the light appearing on screen. It runs in a privileged environment separate from the kernel and blits the light directly onto the screen hardware.
— Guilherme Rambo, in a text message to John Gruber
The point of the blackmail exercise was to have something to describe to policymakers—results that are visceral enough to land with people, and make misalignment risk actually salient in practice for people who had never thought about it before.
— A member of Anthropic’s alignment-science team, as told to Gideon Lewis-Kraus
Use subagents and custom agents in Codex (via) Subagents were announced in general availability today for OpenAI Codex, after several weeks of preview behind a feature flag.
They're very similar to the Claude Code implementation, with default subagents for "explorer", "worker" and "default". It's unclear to me what the difference between "worker" and "default" is but based on their CSV example I think "worker" is intended for running large numbers of small tasks in parallel.
Codex also lets you define custom agents as TOML files in ~/.codex/agents/. These can have custom instructions and be assigned to use specific models - including gpt-5.3-codex-spark if you want some raw speed. They can then be referenced by name, as demonstrated by this example prompt from the documentation:
Investigate why the settings modal fails to save. Have browser_debugger reproduce it, code_mapper trace the responsible code path, and ui_fixer implement the smallest fix once the failure mode is clear.
The subagents pattern is widely supported in coding agents now. Here's documentation across a number of different platforms:
Introducing Mistral Small 4. Big new release from Mistral today (despite the name) - a new Apache 2 licensed 119B parameter (Mixture-of-Experts, 6B active) model which they describe like this:
Mistral Small 4 is the first Mistral model to unify the capabilities of our flagship models, Magistral for reasoning, Pixtral for multimodal, and Devstral for agentic coding, into a single, versatile model.
It supports reasoning_effort="none" or reasoning_effort="high", with the latter providing "equivalent verbosity to previous Magistral models".
The new model is 242GB on Hugging Face.
I tried it out via the Mistral API using llm-mistral:
llm install llm-mistral
llm mistral refresh
llm -m mistral/mistral-small-2603 "Generate an SVG of a pelican riding a bicycle"
I couldn't find a way to set the reasoning effort in their API documentation, so hopefully that's a feature which will land soon.
Also from Mistral today and fitting their -stral naming convention is Leanstral, an open weight model that is specifically tuned to help output the Lean 4 formally verifiable coding language. I haven't explored Lean at all so I have no way to credibly evaluate this, but it's interesting to see them target one specific language in this way.