Didactopus/.update_readmes/20260314_132024__190-didactopus-deployment-policy-and-agent-hooks__README.md

Didactopus Deployment Policy + Agent Hooks Layer

This update extends the dual-lane policy scaffold with two related concerns:

1. Deployment policy settings

   • single-user / private-first
   • team / lab
   • community repository

2. AI learner / agent hook parity

   • explicit API surfaces for agentic learners
   • capability discovery endpoints
   • task-oriented endpoints parallel to the UI workflows
   • access to pack, learner, evaluator, and recommendation workflows without relying on the UI

Why this matters

Didactopus should remain usable in two modes:

• a human using the UI directly
• an AI learner or agentic orchestrator using the API directly

The AI learner should not lose capability simply because a human-facing UI exists. Instead, the UI should be understood as a thin client over API functionality.

What is added

• deployment policy profile model and endpoint
• policy-aware defaults for pack lane behavior
• agent capability manifest endpoint
• agent learner workflow endpoints
• explicit notes documenting API parity with UI workflows

AI learner capability check

This scaffold makes the AI-learner situation clearer:

• yes, the API still exposes the essential learner operations
• yes, pack access, recommendations, evaluator job submission, and learner-state access remain directly callable
• yes, there is now an explicit capability-discovery endpoint so an agent can inspect what the installation supports

Strong next step

• add service-account / non-human agent credentials
• formalize machine-usable schemas for workflows and actions
• add structured action planning endpoint for agentic learners