Anton NesterovAgent skills? How about playbooks?
I borrowed the playbooks concept from Ansible and created a self-improving agent for OpenCode with RAG and long term memory. Initially I created it for infra and process management, but actually It can do everything.
What is a playbook in this agent context? It's the muscle memory. Instead of repeating debug loops everytime the agent can reuse playbooks and actions. RAG techniques allow to save context and input tokens.
Currently testing it on small local models with ollama and the execution part works flawlessly. You need a larger model to write and debug playbooks though.
Self-improving aspects are actually simple. There are two memory layers: Playbooks and Long Term Memory. Long term memory is shared between sessions and the agent decides when it needs to record memories (it's a skill). So everything is reinforced by RAG.
The other aspect is somewhat related to RL. The LLMs API providers may collect the agent execution loops and improve their models, but in this case you can collect personalized data for your org or yourself and finetune models for your experiences.
Overall, after developing and using my own agents, I’ve come to believe they are the future of user experience. Especially if you use voice input.
Everything is in alpha, and things move fast: https://vski.sh/x/vish
#ansible #aiskills #opencode #deno
I borrowed the playbooks concept from Ansible and created a self-improving agent for OpenCode with RAG and long term memory. Initially I created it for infra and process management, but actually It can do everything.
What is a playbook in this agent context? It's the muscle memory. Instead of repeating debug loops everytime the agent can reuse playbooks and actions. RAG techniques allow to save context and input tokens.
Currently testing it on small local models with ollama and the execution part works flawlessly. You need a larger model to write and debug playbooks though.
Self-improving aspects are actually simple. There are two memory layers: Playbooks and Long Term Memory. Long term memory is shared between sessions and the agent decides when it needs to record memories (it's a skill). So everything is reinforced by RAG.
The other aspect is somewhat related to RL. The LLMs API providers may collect the agent execution loops and improve their models, but in this case you can collect personalized data for your org or yourself and finetune models for your experiences.
Overall, after developing and using my own agents, I’ve come to believe they are the future of user experience. Especially if you use voice input.
Everything is in alpha, and things move fast: https://vski.sh/x/vish
#ansible #aiskills #opencode #deno
