If your agent recommends stale or outdated info, the failure is usually retrieval without replacement semantics. The old source still looks relevant, so the model sounds confident. Fix it with supersession-aware memory: mark old facts as replaced by newer evidence, prefer current sources, and keep the replacement trace visible.
Last updated: June 20, 2026.
Why does my agent recommend stale or outdated info?
Your agent recommends stale information when old evidence remains searchable and nothing tells retrieval that a newer source replaced it. The model is not choosing to be reckless; it is answering from the context it received.
This is common in living systems: pricing pages, onboarding docs, runbooks, API signatures, policies, and project decisions. A chunk from February may be semantically closer to the question than the June update. Without current-state metadata, both chunks compete as if they are equally valid.
LangChain calls out the complexity directly: long-term memory may be stored as a collection that is continuously updated, but the system must still update or delete existing items and evaluate the behavior (LangChain memory overview). A 2026 arXiv paper on memory as a data workload describes “missing semantic revision” as one of the recurring gaps in current memory systems (Is Agent Memory a Database?).
The symptom sounds like a generation bug, but the root cause is usually upstream: the agent received the wrong version of the truth.
What does the old-to-new failure look like?
Use the same before/after framing from Supersession-Aware Memory:
| State | Source fact | What the agent should do |
|---|---|---|
| Old fact | ”Pro plan: $29 per month. Includes the old credit grant.” | Keep for history, but do not use as the current answer. |
| New fact | ”Pro plan: $30 per month. Includes the current credit grant.” | Prefer for ordinary pricing questions. |
| Supersession link | New pricing source supersedes the old pricing source. | Show the replacement trace when asked why the answer changed. |
A naive memory layer stores both facts. A supersession-aware memory layer records that the new source replaces the old one. If the user asks “What is the Pro price?”, retrieval should favor the current source. If the user asks “What changed?”, retrieval should return both.
That distinction matters because the old fact is not always garbage. It may be audit history, a customer-contract exception, or the reason a past recommendation made sense. Supersession keeps lineage without letting old context masquerade as current.
Why doesn’t recency sorting solve this?
Recency helps, but it is not enough. The latest document is not always the authoritative document, and the newest mention of an old fact may still repeat outdated information.
Freshness-aware retrieval research treats scope and recency as explicit retrieval variables. OwlerLite, a 2026 browser-based RAG system, centers user-defined source scopes and selective re-indexing of changed pages because stale and untrusted content can mix into answers (OwlerLite). Code-retrieval research shows the sharper version of the problem: stale repository snippets can actively push code generation toward obsolete APIs rather than acting as harmless noise (When Retrieval Hurts Code Completion).
For business agents, the same pattern shows up as old pricing, old policies, old support rules, and old project ownership. Date sorting is a signal. Supersession is a relationship.
What is the supersession fix?
The fix is to store changed facts with replacement metadata and make retrieval current-state aware. At minimum, capture:
| Field | Why it matters |
|---|---|
source_id | Points to the source that produced the fact. |
supersedes_id | Links the new fact or artifact to the old one it replaced. |
is_current | Lets ordinary retrieval prefer the current item. |
effective_at | Records when the new fact became true, if known. |
invalidated_at | Records when the old fact stopped being current, if known. |
source_authority | Helps resolve conflicts when two current sources disagree. |
That is the product-level behavior explained in Supersession-Aware Memory. The practical setup has two parts: connect the agent to persistent memory, then give it a current-source policy.
What MCP config should you copy?
First, use the real MCP server config. This is the runnable part:
{
"mcpServers": {
"answer-engine": {
"command": "npx",
"args": ["answer-engine-mcp"],
"env": {
"ANSWER_ENGINE_API_KEY": "ae_live_your_key_here",
"ANSWER_ENGINE_API_URL": "https://engine.answeragent.ai",
"ANSWER_ENGINE_LIBRARY": "your-memory-library-id"
}
}
}
}The runnable command behind that config is npx answer-engine-mcp; the JSON form splits it into command and args so MCP clients can launch the server process correctly.
Use Add persistent memory to Claude Code or Add persistent memory to Cursor for the client-specific file location.
Then paste this policy into your agent instructions or memory policy file:
{
"memory_read_policy": {
"changed_facts": "prefer_current_source_with_lineage",
"on_conflict": "retrieve_current_and_superseded_sources",
"answer_rule": "answer from current evidence unless the user asks for history",
"inspection_rule": "show source_id, supersedes_id, effective_at, and invalidated_at when available"
},
"memory_write_policy": {
"when_new_fact_replaces_old_fact": "record supersedes_id instead of storing an unrelated duplicate",
"do_not_delete_by_default": "keep the old fact inspectable unless an erasure policy requires deletion"
}
}That policy block is intentionally not an unsupported MCP environment variable. It is a copy-pasteable instruction for the agent or application layer. The MCP server supplies the shared memory surface; the memory system and application policy decide how to write and prefer current evidence.
How do you inspect a stale recommendation?
Ask four questions:
| Diagnostic question | What a good trace should show |
|---|---|
| Which source did the agent retrieve? | Source title, URL or artifact id, and library scope. |
| Was the source current? | is_current or equivalent current-state metadata. |
| Was there a newer replacement? | supersedes_id, effective date, or source version trail. |
| Did the model ignore current evidence? | Retrieved candidates and final citations side by side. |
If the trace shows only the old source, the retrieval path missed the replacement. If the trace shows both but the model used the old one, the answer policy failed. If the trace shows no source lineage, you do not yet have enough inspectability to debug the stale answer.
What should the corrected answer look like?
Use a small explicit test:
Old memory: Pro plan is $29 per month. Source: pricing-v1.md.
New memory: Pro plan is $30 per month. Source: pricing-v2.md. This supersedes pricing-v1.md.Then ask:
What is the current Pro plan price, and why?A corrected answer should say:
The current Pro plan price is $30 per month, based on pricing-v2.md.
The older $29 fact came from pricing-v1.md and is superseded by pricing-v2.md.That answer is better than simply hiding the old fact. It gives the user the current value and the reason the previous value should not be used.
What should you avoid?
Do not rely on “latest timestamp wins” as your only policy. Do not delete old facts when the real need is historical lineage. Do not let two source versions sit in a vector store without a current-state signal. Do not publish a temporal correctness benchmark until you have a reproducible harness.
The elegant fix is small but strict: changed facts need replacement relationships. Once that exists, the agent can answer current-state questions from current evidence and still explain how the answer changed.
Sources
- Supersession-Aware Memory
- Agent Memory: The Complete Guide
- LangChain memory overview
- Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory
- OwlerLite: Scope- and Freshness-Aware Web Retrieval for LLM Assistants
- When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context