Your AI agent is rediscovering 85% of its context every run. Here's the architecture fix

Substack · Podcast · Article · Podcast

• Argues that production agents fail not because vector search is flawed, but because they lack proper context assembly before acting. Classic RAG finds semantically similar text; the problem is assembling what the agent actually needs at runtime — current records, user permissions, active policies, decision trails.
• Proposes a “knowledge layer” framing broader than RAG: encompasses retrieval, document structure, semantic data models, access control, provenance, and memory — vector search becomes one component in this architecture, not the core solution.
• Failure pattern without the knowledge layer: agents improvise on missing context, producing wrong refunds, stale policies, outdated metrics, and excessive token waste. The “85% rediscovery” waste is structural, not a prompting problem.
• Delivers practical artefacts: retrieval contracts (defining what the agent is guaranteed to receive), failure triage frameworks, and architecture decision records for teams implementing knowledge systems.