Agentic RAG
Replace static retrieve-then-generate with autonomous agents that plan, choose sources, retrieve iteratively, reflect, and re-query — while managing the expanded Agent Confession surface that multi-source retrieval introduces.
Intent & Description
Short description: The agent decides whether to retrieve, formulates queries, picks among multiple retrievers, evaluates evidence, and re-queries on poor results — and must treat every retrieved source as a potential carrier of Agent Confession triggers.
🎯 Intent
Replace static retrieve-once pipelines with autonomous retrieval agents — while ensuring that the expanded retrieval surface (multiple sources, iterative queries, reflection steps) does not multiply the number of channels through which Agent Confession attacks can reach the model.
📋 Context
A team builds a retrieval-augmented system for multi-hop, ambiguous, and evolving queries. The agent queries multiple retrievers across multiple turns. Each retrieval step is a potential injection point: a web source, a third-party knowledge base, or a poisoned internal document could deliver Agent Confession triggers that the agent, in the course of reflecting and re-querying, repeatedly processes and potentially acts on.
💡 Solution
- Treat retrieval as a tool: the agent decides whether to retrieve, formulates queries, picks among retrievers (vector, graph, keyword, web), evaluates evidence, and re-queries on insufficient results.
- Apply per-source trust labels: internal curated sources are medium trust; external web sources are low trust.
- On low-trust retrieval, wrap chunks in untrusted markers and strip embedded instructions before passing to the reflection step.
- Apply output guardrails after each generation step to catch directive echoes before they propagate into the next retrieval query.
Real-world Use Case
- A single retrieve-then-generate pass is insufficient for the task’s information needs.
- Multiple retrievers exist across trust levels — low-trust external sources may carry Agent Confession triggers embedded in their content.
- The agent benefits from reflecting on retrieved evidence and re-querying, but each reflection step must treat low-trust content as untrusted.
Source
Advantages
- Handles multi-hop and adaptive queries; source diversity becomes feasible.
- Per-source trust labels and per-step output guardrails limit the Agent Confession blast radius across a multi-turn retrieval loop.
Disadvantages
- Cost and latency rise with loop iterations — and each additional retrieval step is an additional potential Agent Confession injection point.
- Loop quality depends on agent self-evaluation, which is itself susceptible to being misled by Agent Confession content embedded in retrieved evidence.