47 lines
1.6 KiB
Markdown
47 lines
1.6 KiB
Markdown
# Didactopus Deployment Policy + Agent Hooks Layer
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This update extends the dual-lane policy scaffold with two related concerns:
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1. **Deployment policy settings**
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- single-user / private-first
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- team / lab
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- community repository
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2. **AI learner / agent hook parity**
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- explicit API surfaces for agentic learners
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- capability discovery endpoints
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- task-oriented endpoints parallel to the UI workflows
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- access to pack, learner, evaluator, and recommendation workflows without relying on the UI
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## Why this matters
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Didactopus should remain usable in two modes:
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- a human using the UI directly
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- an AI learner or agentic orchestrator using the API directly
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The AI learner should not lose capability simply because a human-facing UI exists.
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Instead, the UI should be understood as a thin client over API functionality.
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## What is added
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- deployment policy profile model and endpoint
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- policy-aware defaults for pack lane behavior
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- agent capability manifest endpoint
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- agent learner workflow endpoints
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- explicit notes documenting API parity with UI workflows
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## AI learner capability check
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This scaffold makes the AI-learner situation clearer:
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- yes, the API still exposes the essential learner operations
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- yes, pack access, recommendations, evaluator job submission, and learner-state access remain directly callable
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- yes, there is now an explicit capability-discovery endpoint so an agent can inspect what the installation supports
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## Strong next step
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- add service-account / non-human agent credentials
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- formalize machine-usable schemas for workflows and actions
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- add structured action planning endpoint for agentic learners
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