That's what it means to be a "people person" in the context of trying to sell a product, yes. Getting within 2 degrees of a decision maker can open up millions for you, while being a rounding error for every company you work with.
I'm hand rolling a project right now because even frontier models I use bloat things beyond comprehension. Because I'm intimately familiar with the domain, I know the shape of things, how the data should flow, and so on, and if l even if I spec it clearly AI will write 2x to 5x the amount of code necessary to make something work.
"beyond comprehension" is a good way of putting it. I've been genuinely baffled by some of these AI designs - why any intelligent thing would write >10 lines of bloat for what should be a one-liner.
> "beyond comprehension" is a good way of putting it. I've been genuinely baffled by some of these AI designs - why any intelligent thing would write >10 lines of bloat for what should be a one-liner.
As Anthropic's drones say: treat Claude as your genius coworker. Don't think yourself, don't judge, the machine must know better than you. It is the genius, after all, not you.
I'm building an agent framework in golang and it is extremely light weight. Startup time is under 1/2 second, and RAM usage is really low. I have a 12 year old laptop and it happily runs without slowing down.
There's no reason what is essentially a string concat engine should be slow on any hardware, including old hardware.
Link is here [0]. The idea is to model cognitive states (how to think), and workflows (what to think about) as statecharts. The charts will be defined in YAML (version-able, hot-reloading). Context payloads are defined in an agent YAML file. Think of it as a map, like a drive map for a computer's HDD/SSD. You spec the order of context chunks, what goes into them, and then when the inference payload is built, it uses the context map definition (comprised of the chunks you defined), the agent definition (including model params like context length, temp, etc), cognitive state, and workflow state to build out the inference payload.
Agent cognitive states may add chunks to the system prompt. Workflows may add chunks to the system prompt. Tool access may vary by agent/workflow state (policy is last-defined-wins overlays to keep it simple to reason about).
Agents may run by themselves or be 'bound' to a workflow. Agents can detach from a workflow before it is finished, and either re-bind, or another agent may bind to the workflow (one implements, another reviews, for example).
Conceptually, this is all very simple, which is why I'm hand rolling it.
The goal is a minimal runtime that can support long-running agents in a 'zero human company' setting.
On top of the runtime will be a minimal change control workflow (if you've spent time in hardware engineering, these are standard processes governed by a company's quality system).
I've yet to wire in the economic pieces (token spend, power consumption, rollups that show performance of various agents based on inputs and outputs).
It is a bit far fetched, but I'd like to get this thing ISO9001 certified, and maybe AS9100 certified.
This is all to scratch my own itch, tbh. Most agentic systems are hard to reason about, bloated, lack visibility in the appropriate places, lack economic data of sufficient granularity, and so on. So I'm building this.
I'm on the opposite end of this spectrum. Puppets are absurd, hysterical, and it used to be a family thing to get together and make puppets every Christmas. I'd try to make the goofiest looking puppet possible. The last one I made has a wide brimmed hat, blond hair, glasses, and the weirdest looking braided mustache. Oh, and ping pong balls for eyes.
I'm working on Maelstrom, an agent framework with only the basics:
- 'agent' as cognitive state, ie, what to think about
- 'workflow' as 'what to think about
- 'session' as immutable agent history
- 'timers' as a way to kick off an agent on a schedule (with or without a workflow attached
I've been working on this since just before OpenClaw dropped at the end of January. Currently it weighs in at around 20k lines of code. There is still a significant amount of work to be done on polish, but the core appears to be functional, and almost to the point where I can replace opencode as my daily driver (I'm very much looking forward to this).
From [1]:
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I've been working on a framework since the end of January or so. I'm on my 7th draft. As I've gone along, each draft gets markedly smaller. The overlaps between what I'm building and openclaw are significant, but I've realized the elements that make up the system are distinct, small, and modular (by design).
There are only a few primitives:
1. session history
1a. context map + rendered context map (think of a drive partitioning scheme, but for context -- you can specify what goes into each block of context and this gets built before being sent out for inference).
2. agent definition / runtime
3. workflow definition / runtime
4. workflow history
5. runtime history (for all the stuff session and workflow history fail to capture because they are at a lower level in the stack)
That's it. Everything else builds on top of these primitives, including
- memory (a new context block that you add to a context map)
- tool usage (which is a set of hooks on inference return and can optionally send the output straight back for inference -- this is a special case inside the inference loop and so just lives there)
- anything to do with agent operating environment (this is an extension of workflows)
- anything to do with governance/provenance/security (this is an extension of either workflows and/or agent operating environment... I haven't nailed this down yet).
I suppose I should say something about how agents and workflows work together. I've broken up 'what to do' and 'how to think' into the two primitives of 'workflow' and 'agent' respectively. An agent's context map will have a section for system prompt and cognitive prompt, and an agent can 'bind' to a workflow. When bound, the agent has an additional field in their context map that spells out the workflow state the agent is in, the available tools, and state exit criteria. Ideally an agent can bind/unbind from a workflow at will, which means long-running workflows are durable beyond just agent activity. There's some nuance here in how session history from a workflow is stored, and I haven't figured that out yet.
Generally, the idea of a workflow allows you to do things like scheduled tasks, user UI, connectors to a variety of comms interfaces, tasks requiring specific outputs, etc. The primitive lays the foundation for a huge chunk of functionality that openclaw and others expose.
It's been fun reasoning through this, and I'll admit that I've had an awful lot of FOMO in the mean time, as I watch so many other harnesses come online. The majority of them look polished, and are well marketed (as far as AI hype marketing goes). But I've managed to stay the course so far.
I hope you find your ideal fit. These tools have the potential to be very powerful if we can manage to build them well enough.
Can't you prevent pushing from the client side with pre-commit hooks? I would expect a hook to fire on the developer's computer that prevents them from even committing/pushing (unless they nuke the hook in their local repo copy).
You have to manually install hooks in your local repository. They aren't propagated as part of the repo. Git has intentionally made hooks require a very explicit opt-in.
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