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.
