Notes from project work
Notes on intent-aware, graph-based context exchange between agents for traceability and reliability.
ICN starts from a practical observation: agent workflows become unreliable when intent and context are implicit.
The core idea is to treat intent as a structured artifact and context as a queryable graph rather than a chat transcript.
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Why operational AI needs auditability, boundaries, and deployment flexibility more than autonomy.
Operational AI is high stakes. If you cannot trace decisions, you cannot trust automation.
RitualOps AI treats commercialization readiness as a core constraint: multi-tenant, SaaS/on-prem friendly, and auditable workflows.
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A case for explicit intent objects, validation layers, and traceable execution in AI-assisted workflows.
Most automation failures are intent failures: unclear scope, hidden constraints, and ambiguous asks.
An intent layer makes goals and constraints explicit, enabling validation and safe execution patterns.
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A practical set of RAG patterns focused on grounding, structure, and evaluation.
RAG is not a feature; it is a reliability strategy.
The patterns that matter most: schema-first outputs, retrieval hygiene, caching, and evaluation loops.
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