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AI assistant making up CRM data — investigate hallucination source #39

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opened 2026-05-06 17:07:25 +00:00 by mik-tf · 4 comments

mik-tf commented

2026-05-06 17:07:25 +00:00

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Overview

The CRM AI surface returns invented contacts / projects / tasks instead of grounding on real OSIS data. Casper hit this; Timur is investigating.

Why

Meeting 2026-05-06: "asked timur to view the issue / AI making up stuff / explained to Casper / Timur helping Casper on blockers".

This is related to but distinct from home#215 (assistant non-functional from missing Groq key). The hallucination here happens even when the assistant responds — the response is fluent but factually wrong.

Likely candidates for the root cause:

Per-context routing broken (hero_biz#37 + hero_rpc#42) — agent gets default-context data instead of the user's context, so its grounding is wrong.
Tool surface for CRM reads/writes missing or unreliable — agent falls back to generative answer.
System prompt doesn't enforce "if you don't have grounded data, say so".

Acceptance

Reproduce a clear hallucination case (one prompt, one wrong response)
Identify which of the candidate causes is the dominant one
Fix it (filed as separate issue if non-trivial) or escalate to hero_agent
Verify on demo VM with the same prompt

hero_biz#37 — context selection has no effect
hero_rpc#42 — UDS transport drops X-Hero-Context
home#215 — assistant non-functional
hero_agent#16 — Ambient AI parent

Owner: timur (investigating) + casper (reporting).

Source: meeting notes 2026-05-06.

## Overview The CRM AI surface returns invented contacts / projects / tasks instead of grounding on real OSIS data. Casper hit this; Timur is investigating. ## Why Meeting 2026-05-06: "asked timur to view the issue / AI making up stuff / explained to Casper / Timur helping Casper on blockers". This is *related to but distinct from* [home#215](https://forge.ourworld.tf/lhumina_code/home/issues/215) (assistant non-functional from missing Groq key). The hallucination here happens **even when the assistant responds** — the response is fluent but factually wrong. Likely candidates for the root cause: 1. Per-context routing broken ([hero_biz#37](https://forge.ourworld.tf/lhumina_code/hero_biz/issues/37) + [hero_rpc#42](https://forge.ourworld.tf/lhumina_code/hero_rpc/issues/42)) — agent gets default-context data instead of the user's context, so its grounding is wrong. 2. Tool surface for CRM reads/writes missing or unreliable — agent falls back to generative answer. 3. System prompt doesn't enforce "if you don't have grounded data, say so". ## Acceptance - [ ] Reproduce a clear hallucination case (one prompt, one wrong response) - [ ] Identify which of the candidate causes is the dominant one - [ ] Fix it (filed as separate issue if non-trivial) or escalate to hero_agent - [ ] Verify on demo VM with the same prompt ## Related - [hero_biz#37](https://forge.ourworld.tf/lhumina_code/hero_biz/issues/37) — context selection has no effect - [hero_rpc#42](https://forge.ourworld.tf/lhumina_code/hero_rpc/issues/42) — UDS transport drops X-Hero-Context - [home#215](https://forge.ourworld.tf/lhumina_code/home/issues/215) — assistant non-functional - [hero_agent#16](https://forge.ourworld.tf/lhumina_code/hero_agent/issues/16) — Ambient AI parent Owner: timur (investigating) + casper (reporting). Source: meeting notes 2026-05-06.

mik-tf added this to the ACTIVE project

2026-05-06 17:31:55 +00:00

mik-tf referenced this issue from lhumina_code/home

2026-05-06 17:45:03 +00:00

[ROADMAP] Phase ordering for ACTIVE board (project 13) #221

~~casper-stevens referenced this issue 2026-05-07 07:42:06 +00:00~~

fix(rpc): UDS transport silently drops X-Hero-Context headers — context isolation broken for direct socket calls #40

casper-stevens commented

2026-05-07 07:53:47 +00:00

Member

The dominant cause is likely candidate 2 — the AI tool surface may not exist yet. From reading the code, there appear to be no tool definitions, no function-calling schema, and no OSIS calls from the AI layer. build_entity_context() loads the currently-viewed entity and injects it as markdown into the prompt; that's likely the only OSIS data the LLM sees. Anything outside that one entity would then get fabricated.

Candidate 1 (context routing) is likely not the cause here. hero_biz → hero_osis traffic goes over HTTP through hero_router, and that path appears to already pass X-Hero-Context correctly. The UDS header bug in hero_rpc#42 is real but likely affects a different code path.

What would unblock this: wire CRM read tools into the assistant (search_persons, search_companies, list_tasks, get_deal) with a proper function-calling schema and dispatch in the assistant_chat() handler. Adding a grounding guardrail to the system prompt would help as a safety net regardless.

The dominant cause is **likely candidate 2** — the AI tool surface may not exist yet. From reading the code, there appear to be no tool definitions, no function-calling schema, and no OSIS calls from the AI layer. `build_entity_context()` loads the currently-viewed entity and injects it as markdown into the prompt; that's likely the only OSIS data the LLM sees. Anything outside that one entity would then get fabricated. **Candidate 1 (context routing)** is likely not the cause here. hero_biz → hero_osis traffic goes over HTTP through hero_router, and that path appears to already pass `X-Hero-Context` correctly. The UDS header bug in hero_rpc#42 is real but likely affects a different code path. **What would unblock this:** wire CRM read tools into the assistant (`search_persons`, `search_companies`, `list_tasks`, `get_deal`) with a proper function-calling schema and dispatch in the `assistant_chat()` handler. Adding a grounding guardrail to the system prompt would help as a safety net regardless.

casper-stevens referenced this issue from lhumina_code/hero_osis

2026-05-07 08:01:11 +00:00

fix: update Cargo.lock to pick up hero_rpc_client context fix (ed6e7eb3c0) #46

casper-stevens commented

2026-05-11 14:54:22 +00:00

Member

Implementation Spec — Issue #39: Ground the AI Assistant on Real OSIS Data

Objective

Eliminate hallucination from the assistant_chat endpoint by dispatching Store queries based on the parsed Intent and injecting the results into the LLM prompt before the main model call. Add a system prompt guardrail instructing the LLM to refuse to answer from memory when data is not present in context.

Root Cause (confirmed)

process_message in assistant.rs calls analyze_intent and receives a correctly-populated Intent struct, then discards it — no store query is dispatched. The LLM receives only the single currently-viewed entity's markdown (context_data) and must answer all broader queries from its parametric memory, which it fabricates.

Requirements

After analyze_intent returns, inspect intent.action and intent.entity_type to decide what additional data to fetch from the Store.
Fetch that data before constructing the main LLM prompt.
Append a ## Retrieved Data section to the system prompt containing the fetched records as compact markdown (capped at 10 results).
Add a hard guardrail rule to the system prompt: the assistant must state "I don't have that data in my current context" rather than invent facts.
The dispatch logic lives in the handler (assistant_chat), not in Assistant::process_message, so Assistant's API surface stays unchanged except for one new method.
Pass the augmented context back through the existing AssistantContext.context_data field.

Files to Modify

crates/hero_biz_admin/src/ai/assistant.rs — add guardrail rule to build_system_prompt; add process_message_preanalyzed to avoid double intent-analysis LLM call
crates/hero_biz_admin/src/web/handlers/mod.rs — add dispatch_intent_to_store helper; wire it into assistant_chat

Implementation Plan

Step 1 — Guardrail + `process_message_preanalyzed` in `assistant.rs`

File: crates/hero_biz_admin/src/ai/assistant.rs

In build_system_prompt, append rule 7 to the INSTRUCTIONS block:

7. CRITICAL: Only reference facts that appear in the CURRENT CONTEXT or RETRIEVED DATA sections above.
   If the information the user is asking about is not present in those sections, respond with:
   "I don't have that information in my current context." Never invent names, IDs, amounts, dates, or relationships.

In the analyze_intent system prompt, explicitly list valid action values:
"search", "list", "get_info", "get_related", "add_investment", "update_contact", "create_deal", "ask_question".
Add process_message_preanalyzed(&self, user_input, context, conversation_history, intent: Option<Intent>) -> Result<AssistantResponse>. This is the current process_message body with the internal analyze_intent call replaced by the passed-in intent. Refactor process_message to delegate to process_message_preanalyzed.

Step 2 — `dispatch_intent_to_store` + wiring in `handlers/mod.rs`

File: crates/hero_biz_admin/src/web/handlers/mod.rs

Add async fn dispatch_intent_to_store(store, searcher, ctx, intent, current_context_type, current_context_id) -> Option<String> near build_entity_context. It:

On action == "search": calls searcher.search / searcher.search_type and formats up to 10 results.
On action == "list" | "get_info" | "get_related": calls the appropriate Store method based on entity_type and optional entity_id (uses relationship methods when an ID is present).
Returns a compact markdown block to append as ## Retrieved Data.

In assistant_chat, before calling process_message:

Call assistant.analyze_intent up-front.
Call dispatch_intent_to_store with the result.
Merge the returned markdown into context_data (append \n\n## Retrieved Data\n{extra}).
Call assistant.process_message_preanalyzed with the augmented context and the already-fetched intent (avoids a second intent-analysis LLM call).

Acceptance Criteria

"List all deals" returns actual deal names from the store
"Find persons named X" triggers fuzzy search; results appear in the response
"What deals does [current person] have?" calls get_deals_for_person, results cited correctly
Asking about non-existent data → assistant says "I don't have that information in my current context"
No double LLM calls for intent analysis
cargo build passes with no new warnings

Notes

AppState already carries both store and searcher; no struct changes needed.
Searcher in search/fuzzy.rs covers all entity types.
Token budget: 10 results × ~80 chars ≈ 200 tokens — well within budget.
No new modules or traits required. All changes are additions to existing functions in two files.

## Implementation Spec — Issue #39: Ground the AI Assistant on Real OSIS Data ### Objective Eliminate hallucination from the `assistant_chat` endpoint by dispatching `Store` queries based on the parsed `Intent` and injecting the results into the LLM prompt before the main model call. Add a system prompt guardrail instructing the LLM to refuse to answer from memory when data is not present in context. ### Root Cause (confirmed) `process_message` in `assistant.rs` calls `analyze_intent` and receives a correctly-populated `Intent` struct, then discards it — no store query is dispatched. The LLM receives only the single currently-viewed entity's markdown (`context_data`) and must answer all broader queries from its parametric memory, which it fabricates. ### Requirements 1. After `analyze_intent` returns, inspect `intent.action` and `intent.entity_type` to decide what additional data to fetch from the `Store`. 2. Fetch that data before constructing the main LLM prompt. 3. Append a `## Retrieved Data` section to the system prompt containing the fetched records as compact markdown (capped at 10 results). 4. Add a hard guardrail rule to the system prompt: the assistant must state "I don't have that data in my current context" rather than invent facts. 5. The dispatch logic lives in the handler (`assistant_chat`), not in `Assistant::process_message`, so `Assistant`'s API surface stays unchanged except for one new method. 6. Pass the augmented context back through the existing `AssistantContext.context_data` field. ### Files to Modify - `crates/hero_biz_admin/src/ai/assistant.rs` — add guardrail rule to `build_system_prompt`; add `process_message_preanalyzed` to avoid double intent-analysis LLM call - `crates/hero_biz_admin/src/web/handlers/mod.rs` — add `dispatch_intent_to_store` helper; wire it into `assistant_chat` ### Implementation Plan #### Step 1 — Guardrail + `process_message_preanalyzed` in `assistant.rs` File: `crates/hero_biz_admin/src/ai/assistant.rs` - In `build_system_prompt`, append rule 7 to the INSTRUCTIONS block: ``` 7. CRITICAL: Only reference facts that appear in the CURRENT CONTEXT or RETRIEVED DATA sections above. If the information the user is asking about is not present in those sections, respond with: "I don't have that information in my current context." Never invent names, IDs, amounts, dates, or relationships. ``` - In the `analyze_intent` system prompt, explicitly list valid `action` values: `"search"`, `"list"`, `"get_info"`, `"get_related"`, `"add_investment"`, `"update_contact"`, `"create_deal"`, `"ask_question"`. - Add `process_message_preanalyzed(&self, user_input, context, conversation_history, intent: Option<Intent>) -> Result<AssistantResponse>`. This is the current `process_message` body with the internal `analyze_intent` call replaced by the passed-in `intent`. Refactor `process_message` to delegate to `process_message_preanalyzed`. #### Step 2 — `dispatch_intent_to_store` + wiring in `handlers/mod.rs` File: `crates/hero_biz_admin/src/web/handlers/mod.rs` Add `async fn dispatch_intent_to_store(store, searcher, ctx, intent, current_context_type, current_context_id) -> Option<String>` near `build_entity_context`. It: - On `action == "search"`: calls `searcher.search` / `searcher.search_type` and formats up to 10 results. - On `action == "list" | "get_info" | "get_related"`: calls the appropriate `Store` method based on `entity_type` and optional `entity_id` (uses relationship methods when an ID is present). - Returns a compact markdown block to append as `## Retrieved Data`. In `assistant_chat`, before calling `process_message`: 1. Call `assistant.analyze_intent` up-front. 2. Call `dispatch_intent_to_store` with the result. 3. Merge the returned markdown into `context_data` (append `\n\n## Retrieved Data\n{extra}`). 4. Call `assistant.process_message_preanalyzed` with the augmented context and the already-fetched intent (avoids a second intent-analysis LLM call). ### Acceptance Criteria - [ ] "List all deals" returns actual deal names from the store - [ ] "Find persons named X" triggers fuzzy search; results appear in the response - [ ] "What deals does [current person] have?" calls `get_deals_for_person`, results cited correctly - [ ] Asking about non-existent data → assistant says "I don't have that information in my current context" - [ ] No double LLM calls for intent analysis - [ ] `cargo build` passes with no new warnings ### Notes - `AppState` already carries both `store` and `searcher`; no struct changes needed. - `Searcher` in `search/fuzzy.rs` covers all entity types. - Token budget: 10 results × ~80 chars ≈ 200 tokens — well within budget. - No new modules or traits required. All changes are additions to existing functions in two files.

casper-stevens commented

2026-05-11 15:14:09 +00:00

Member

Test Results

Status: PASS
Tests run: 7
Passed: 7
Failed: 0

All tests passed. Tests are in the hero_biz_admin crate:

services::tests::path_traversal_sequences_rejected
services::tests::null_byte_is_rejected
services::tests::empty_string_is_rejected
services::tests::dot_dot_is_rejected
services::tests::slash_is_rejected
parser::tests::test_name_fix
services::tests::valid_components_pass

## Test Results - Status: PASS - Tests run: 7 - Passed: 7 - Failed: 0 All tests passed. Tests are in the `hero_biz_admin` crate: - `services::tests::path_traversal_sequences_rejected` - `services::tests::null_byte_is_rejected` - `services::tests::empty_string_is_rejected` - `services::tests::dot_dot_is_rejected` - `services::tests::slash_is_rejected` - `parser::tests::test_name_fix` - `services::tests::valid_components_pass`

casper-stevens commented

2026-05-11 15:14:53 +00:00

Member

Implementation Summary

Root Cause

process_message in assistant.rs called analyze_intent and received a correctly-populated Intent struct, then discarded it — no store query was ever dispatched. The LLM received only the single currently-viewed entity's markdown and had to answer all broader queries from its parametric memory, producing fabricated results.

Changes Made

crates/hero_biz_admin/src/ai/assistant.rs

Added rule 7 to the INSTRUCTIONS block in build_system_prompt: hard guardrail forbidding the LLM from inventing names, IDs, amounts, dates, or relationships not present in CURRENT CONTEXT or RETRIEVED DATA
Enumerated valid action values in the analyze_intent system prompt so the LLM reliably emits a dispatchable action string
Added process_message_preanalyzed method that accepts a pre-computed Option<Intent>, avoiding a double intent-analysis LLM call when the caller has already run intent analysis
Refactored process_message to delegate to process_message_preanalyzed

crates/hero_biz_admin/src/web/handlers/mod.rs

Added dispatch_intent_to_store async helper that maps Intent.action + Intent.entity_type to concrete Store queries (list, relationship lookup, or fuzzy search via Searcher)
Results are formatted as compact markdown (up to 10 entries) and appended as a ## Retrieved Data section in the prompt
Wired into assistant_chat: intent is now analysed up-front, dispatched to the store, and the augmented context is passed to process_message_preanalyzed — a single intent-analysis LLM call per request

Test Results

7/7 tests passed (cargo test, hero_biz_admin crate).

## Implementation Summary ### Root Cause `process_message` in `assistant.rs` called `analyze_intent` and received a correctly-populated `Intent` struct, then discarded it — no store query was ever dispatched. The LLM received only the single currently-viewed entity's markdown and had to answer all broader queries from its parametric memory, producing fabricated results. ### Changes Made **`crates/hero_biz_admin/src/ai/assistant.rs`** - Added rule 7 to the INSTRUCTIONS block in `build_system_prompt`: hard guardrail forbidding the LLM from inventing names, IDs, amounts, dates, or relationships not present in CURRENT CONTEXT or RETRIEVED DATA - Enumerated valid `action` values in the `analyze_intent` system prompt so the LLM reliably emits a dispatchable action string - Added `process_message_preanalyzed` method that accepts a pre-computed `Option<Intent>`, avoiding a double intent-analysis LLM call when the caller has already run intent analysis - Refactored `process_message` to delegate to `process_message_preanalyzed` **`crates/hero_biz_admin/src/web/handlers/mod.rs`** - Added `dispatch_intent_to_store` async helper that maps `Intent.action` + `Intent.entity_type` to concrete `Store` queries (list, relationship lookup, or fuzzy search via `Searcher`) - Results are formatted as compact markdown (up to 10 entries) and appended as a `## Retrieved Data` section in the prompt - Wired into `assistant_chat`: intent is now analysed up-front, dispatched to the store, and the augmented context is passed to `process_message_preanalyzed` — a single intent-analysis LLM call per request ### Test Results 7/7 tests passed (cargo test, hero_biz_admin crate).

casper-stevens referenced this issue from a commit

2026-05-11 16:21:30 +00:00

feat(hero_biz_admin): ground AI assistant on real OSIS data (closes #39)

casper-stevens closed this issue

2026-05-11 16:21:30 +00:00