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URL?

I'd love to know if people are using CEL, and what for. Any war stories to share?

LA Metro: Ambient turns the Los Angeles rail network into a generative music instrument. Every note you hear is a real train arriving at a real station right now. Each of the six rail lines and two BRT lines has its own synthesizer voice. The piece evolves throughout the day as trains and buses come online and go offline, becoming dense during rush hour and more spacious late at night. The piece is ever changing and no two days are the same.

I haven’t dropped an agent into production yet. This is all internal / PoC work.

Claude / your favourite LLM coding agent can help you design this and will be good at helping you unpack risks and hazards. Good luck!

I’m dealing with invoices and customers and timesheets and tickets. I didn’t want to give an agent SQL access to the database for performance and security reasons, and we don’t have a read only replica (which would have been much more acceptable to give read access to). So my tools are get_invoices etc. and they pull data into the SQLite.

My use case is a short conversation to answer a question like “has all the time from this task been invoiced?” Or “what contributed to the size of this invoice!”. So my agent starts with empty SQLite, fetches the data into SQLite, writes queries to answer the question, then user can continue to interrogate but eventually shuts down.

I don’t have a perpetually running agent so I haven’t had to solve that problem.

What could go wrong? Delete production data. Corrupt production data. Show staff member info they shouldn’t have. Show customer info they shouldn’t have. Rely too much on the LLM doing the right thing, it doesn’t, and the agent presents the wrong information without knowing. Bugger up the security and anyone can use the agent to query production data. … Yeah, be cautious.

Fast is probably going to get in the way of Good, but here are some things to think about.

1. In many businesses, different users of the agent should have different read/write permissions to data.

2. Log everything. There will be problems. You'll need to debug, attribute, defend yourself against accusations that the cockup was your fault, etc.

3. If you're just providing read-only then the more you can enforce that at all layers of the stack (e.g., read-only user in the database).

4. Don't give the agent direct SQL access to the database. Write functions in your favourite programming language, expose them as tools to the LLM.

5. Don't make your LLM do maths in its head, they're crap at it. I had some success giving the agent tools that fetch data from the database (where I control the SQL that runs) into a SQLite in agent memory, then the agent can run SQL against that database. Gives you a chance to put the data into an obvious, easy to query, format rather than whatever arcane historic chaotic state your main schema has evolved to).

Nice! Claude and I made something for my use called 'azdo' that's the 'gh' CLI but for Azure DevOps. I figured agents know how to use gh, so give them what they know. I haven't yet tried aliasing so they don't (at first) know it's Azure DevOps but that's coming ...

I like it a lot. AND it has MCP server so I can point my agent at it to search and research. Very impressive.

Being able to mange context over long running sessions is a function of the harness, not the model. Are you using Claude Code with GPT5.5? Codex? piclaw? They’ll all have different context management strategies to let you keep going when you would otherwise have filled up context and be forced to stop.

This repo's README explains how it works and you can do it yourself. claude looks for environment variables that say which API endpoint to talk to, which key to pass, which model name to use for haiku/sonnet/opus-level workloads, etc.

Forgot to add: I get several benefits from doing this.

1. Specifications that live outside the code. We have a lot of code for which "what should this do?" is a subjective answer, because "what was this written to do?" is either oral legend or lost in time. As future Claude sessions add new features, this is how Claude can remember what was intentional in the existing code and what were accidents of implementation. And they're useful for documenters, support, etc.

2. Specifications that stay up to date as code is written. No spec survives first contact with the enemy (implementation in the real world). "Huh, there are TWO statuses for Missing orders, but we wrote this assuming just one. How do we display them? Which are we setting or is it configurable?" etc. Implementer finds things the specifier got wrong about reality, things the specifier missed that need to be specified/decided, and testing finds what they both missed.

I have a colleague working on saving architecture decisions, and his description of it feels like a higher-abstraction version of my saving and maintaining requirements.