Interactive lessons — hosted on teach.zahranm.cloud
The architecture of a tool-calling autonomous agent: the conversation loop, tool registry, system prompt assembly, session persistence, gateway pattern, and the five sacred rules.
How Hermes turns one user message into repeated model calls, tool dispatch, recovery, context compression, and a final answer.
How Hermes manages its capability surface across three layers: the tool registry (built-in tools), MCP servers (external tools), and skills (procedural knowledge).
How the system prompt is built from identity, skills, memory, context files, and platform hints.
How conversations are stored, searched, and compressed. The parent_session_id chain pattern.
How one agent core serves 20+ messaging platforms through adapter interfaces.
The self-improvement loop: declarative memory vs procedural skills, and how an agent learns across sessions.
Putting it all together: a working ~200-line Python agent with tools, sessions, and a CLI.
How Bella Enterprise currently decomposes Web, API, Worker, WhatsApp Bridge, Hindsight, external stores, and per-service deployment guarantees. Updated against MetatechID/bella-enterprise main.
Bella's typed action surface: how both brains share one verb catalog (9 → 227 verbs), ADR 0010 stateful invertibility, Surface adapters, browser verbs, engine parity, Composio, and external-agent delegation.
ASCII diagram mapping all six agentic flows in cv-search.zahranm.cloud: async extraction pipeline (Gemini OCR → pdftotext → LLM → regex fallback), recruiter search, JD matching, job polling, PDF dataset seeding, and CRUD/browse.