For me the heart of an agentic system is NOT using agents (except when you really have to). Components of a working system include: - Pipelines/recipes to describe multi-step flows (deterministic, agentic and HiTL steps), loops, conditionals, exit-on's for max loop iteractions, etc - The logistics to actually run the model and HiTL steps reliably across multiple agent worker pools - Management and delivery (and security/governance and permissioning) of thick skills with code to do as much as possible - Context management so the right agents have the right context for the right sessions at the right time - Project management - ability to store and access tickets, dependencies, track progress, restart stuck ticket claims, etc - Transcript saving, memory features and dreaming/compounding capabilities so the agents continue to learn from each session - o11y for understanding whats happening, tracking costs and usages, etc - Evals and auto-tuning of prompts so you can go cross model provider and also lock to a model version so you can do an ROI on each model version upgrade - Sandboxes for running the actual model sessions
Don't need to get it all from one vendor, but that feels to me like the toolkit and for most use cases I'd argue: - Don't limit yourself to a single model provider (anthropic, openai, etc) - Own your context - Own your compounding
Can you comment more on
> Context management so the right agents have the right context for the right sessions at the right time
I'm going to do a show HN tomorrow that explains how you can give your agents years of experience. The basic idea is, you would commit in your repo or download manifests (JSON files) that can be converted to "Brains" (SQLite databases). Each brain can have its own properties.
For example, I provide a "code intent" analyzer (instructions for AI) that says when analyzing a file, extract this metadata. For the code intent analyzer, I have the AI extract a single sentence purpose for the file. So if you execute:
gsc rg cache --db code-intent --fields purpose
you get all matches for 'cache' plus the matching file's purpose like "Modify file to update caching strategy". This is how the agent can tell if the file is talking about cache vs. whether this file is what you should change if you want to update the caching strategy.
So for what you described, you can have a brain for different stages of a task. It can be as simple as, in the planning stage, make sure you do this if you need to touch this file.
I am working on a rust-blast-radius brain that uses `syn` + AI generated metadata to help you understand "what if I changed this file, what would be affected". With the rust-blast-radius brain, the AI can summarize the types of files that will be affected without having to open the file based on what has been changed or discussed.
So you can have a rule like, if I make changes to a Rust file, make sure to do a blast radius analysis so we don't forget to consider something.
Does this align with what you are looking for?
(I'm not the guy but) That's funny, I had the same idea the other day. Keeping summaries of files. Haven't tested that yet.
Another thing I've been thinking is how, most parts of a file are not relevant to the whole system.
Like there are parts where they intersect, and those seem to be the most important ones for capturing the big picture. You wanna be able to see the entire "skeleton".
So I thought the summary maybe shouldn't be English but it should be a subset of the code — the subset that's relevant to the rest of the program.
`grep import` gets you 90% of the way there.
If you include the following:
https://github.com/gitsense/chat/blob/main/base-state/analyz...
In your chat with AI, include the above file and let it know what your requirements are and I can create the analyzer and include it.
You can also think of my tool as data prepping tool. So if you have a clear prompt the AI can review the file during analysis and remove all unnecessary code so the extracted metadata will the stripped text which you can use search against.