Most teams scaling beyond simple RAG hit the same wall.
In a standard RAG setup, retrieval is a function:
q → retrieve(k) → generate
You control the query, the index, and the ranking. Evaluation is straightforward because the system is shallow.
With agents, retrieval becomes iterative and model-driven:
q₀ → retrieve → state₁
state₁ → q₁ → retrieve → state₂
state₂ → q₂ → retrieve → …
The model is now generating queries conditioned on intermediate state, not original intent. That’s a fundamentally different regime.
Two things follow from this:
- Query quality becomes stochastic
- Error compounds across steps
Neither of these are new problems individually, but what changes is that they now sit inside the same loop.
Why Agentic systems based queries underperform
There’s a consistent pattern across production systems: LLM-generated queries are syntactically cleaner and semantically worse for retrieval.
Agentic systems tend to:
- normalize away domain-specific tokens that anchor results
- over-generalize early in the reasoning chain
- optimize for linguistic completeness rather than discriminative power
From a retrieval perspective, this reduces recall in ways that are hard to detect unless you inspect query logs directly.
You can end up retrieving documents that are “about the topic,” but not the ones that actually resolve the task.
In a single-step pipeline, this is manageable, less so in multi-step agent loops.
The failure mode
If step one is slightly off, step two reinforces that framing. Standard evaluation pipelines miss this because they focus on final output quality, not intermediate retrieval states.
Long context makes this harder to notice
Long context windows mask retrieval issues rather. Instead of selecting the right documents, systems start passing more documents. The model then implicitly ranks them through attention.
This introduces two new issues:
- attention is not a stable ranking function under scale
- irrelevant context can dilute signal
So instead of retrieval failing explicitly, the system still produces answers, but grounding becomes inconsistent across runs.
The fix: Separate retrieval from reasoning again
Concretely:
Instead of letting the agent freely generate queries at each step, you introduce a constrained retrieval layer:
stateₙ → query projection → retrieval → validation → stateₙ₊₁
Each of these steps needs to be explicit.
1. Query projection
Introduce a projection layer in your agentic system that:
- enforces schema (entities, constraints, time bounds)
- preserves domain-specific tokens
- limits abstraction (no early generalization)
This can be implemented as:
- a smaller model fine-tuned for query rewriting
- or a constrained decoding setup with templates
The key is that query formation becomes deterministic within bounds.
2. Hybrid retrieval
Pure vector search underperforms in multi-step reasoning because semantic similarity drifts across steps.
You need:
- vector search for semantic recall
- lexical search (BM25 or similar) for token anchoring
Blending them stabilizes recall when phrasing shifts.
3. Retrieval validation before state update
Insert a validation step that:
- scores relevance against the original task, not just the current state
- filters documents that match intermediate drift but not root intent
4. Forced re-grounding
Introduce explicit checkpoints where:
- the system re-queries against the original objective
- compares current context vs fresh retrieval
- resolves divergence
What this changes
This approach makes retrieval predictable again, and you can now constrain how queries evolve in your agentic systems. You stabilize recall across steps and reduce the system’s tendency to converge on internally consistent but externally incorrect answers.
