Multi-Turn Reasoning Breaks

The Problem

Agent fails to maintain reasoning across multiple conversation turns, losing logical thread or failing to build on previous conclusions.

Symptoms

• ❌ Cannot chain reasoning across turns

• ❌ Forgets intermediate conclusions

• ❌ Restarts reasoning from scratch each turn

• ❌ Doesn't build on previous analysis

• ❌ Multi-step problem-solving fails

Real-World Example

Turn 1:
User: "Does Alice have access to Production DB?"
Agent: "Let me check. Alice is in the Engineering team."

Turn 2:
User: "And does Engineering have access?"
Agent: "Yes, Engineering team has access to Production DB."

Turn 3:
User: "So can Alice access it?"
Agent: "I need to check if Alice is in a team with Production DB access."

Problem:
→ Agent forgot Turn 1 conclusion (Alice in Engineering)
→ Agent forgot Turn 2 conclusion (Engineering has access)
→ Cannot chain: Alice → Engineering → Prod DB
→ Reasoning broke

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Deep Technical Analysis

Reasoning State Management

Inference Tracking:

Track established facts across turns:
Turn 1: Established: Alice ∈ Engineering
Turn 2: Established: Engineering → Prod DB access
Turn 3: Query: Alice → Prod DB?

Reasoning:
→ Alice ∈ Engineering (from Turn 1)
→ Engineering → Prod DB (from Turn 2)
→ Therefore: Alice → Prod DB ✓

Requires explicit fact tracking

Intermediate Conclusions:

Store intermediate results:
{
  "facts_established": [
    {"turn": 1, "fact": "Alice is in Engineering team"},
    {"turn": 2, "fact": "Engineering team has Prod DB access"}
  ],
  "pending_inference": "Can Alice access Prod DB?"
}

Next turn: Use stored facts
→ Don't re-derive
→ Build on them

Context Preservation

Reasoning Chain Loss:

Without explicit tracking:
→ Each turn processes independently
→ LLM doesn't automatically remember reasoning
→ Must explicitly include past reasoning in context

Solution:
Pass reasoning summary:
"Previous reasoning: Alice is in Engineering. Engineering has Prod DB access."

Partial Reasoning:

User builds complex question across turns:
Turn 1: "Show me Alice's team"
Turn 2: "What databases can that team access?"
Turn 3: "Filter for production ones"

Agent must track:
→ "that team" = "Alice's team" = "Engineering"
→ "production ones" = filter from Turn 2 results
→ Multi-turn query construction

Fact Accumulation

Evidence Gathering:

Complex question requires multiple facts:
"Can Alice deploy to production?"

Needs:
1. Alice's role (Turn 1: Engineer)
2. Engineer permissions (Turn 2: Can deploy to staging)
3. Production restrictions (Turn 3: Only Senior+ to prod)
4. Alice's level (Turn 4: Senior Engineer)

Conclusion (Turn 5):
→ Combine all facts
→ Answer: Yes (Senior Engineer can deploy to prod)

Requires accumulating evidence across turns

Decomposition Consistency

Sub-Question Tracking:

Original: "Compare X vs Y for our use case"

Agent decomposes:
Turn 1: "Tell me about X"
Turn 2: "Tell me about Y"
Turn 3: "What's our use case?"
Turn 4: Synthesize comparison

Agent must remember:
→ Still working on original question
→ Sub-questions serve main goal
→ Track progress

Goal Maintenance:

User's overarching goal:
"Set up authentication"

Across conversation:
Turn 1: Explained OAuth
Turn 2: Provided credentials
Turn 3: Troubleshooting error

Agent remembers:
→ Goal: Set up authentication
→ Progress: 70% complete
→ Next: Resolve current error
→ Then: Verify auth works

Goal-oriented conversation

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How to Solve

Store established facts from each turn + track intermediate conclusions explicitly + include reasoning summary in context ("Previously established: ...") + implement fact accumulation (gather evidence across turns) + maintain conversation goal state + use chain-of-thought prompting with memory + test multi-turn reasoning scenarios (3+ step inferences) + measure reasoning consistency across turns. See Multi-Turn Reasoning.