Agent Behavior & Strategy

Overview

AI agents are more than just retrieval + generation—they make strategic decisions about how to handle queries, what context to assemble, when to use tools, and how to maintain coherent multi-turn conversations. Agent behavior is where RAG systems transition from simple Q&A to sophisticated assistants. However, agents introduce new failure modes: poor strategy selection, memory corruption, context loss, and unpredictable behavior. This section addresses challenges specific to agent logic and decision-making.

Why Agent Behavior Matters

Well-functioning agents provide:

• Intelligent routing - Right strategy for each query type

• Conversational coherence - Natural multi-turn interactions

• Context awareness - Remember and build on previous exchanges

• Appropriate tool use - Know when and how to use external capabilities

• Consistent persona - Reliable tone and behavior

Poorly functioning agents lead to:

• Strategy selection failures - Wrong approach for the query

• Context loss - Forget previous conversation turns

• Memory corruption - Conflate information across sessions

• Reasoning breaks - Fail on multi-step questions

• Persona drift - Inconsistent tone or behavior

• Tool misuse - Call wrong tools or misinterpret results

Common Agent Challenges

Strategy & Decision-Making

• Strategy selection failures - Wrong retrieval or reasoning approach

• Redwood vs Cedar confusion - Misapply dense vs sparse retrieval strategies

• Context assembly logic issues - Assemble context poorly for LLM

Memory & State

• Agent memory corruption - Mix information across users or sessions

• Conversational context loss - Forget earlier parts of conversation

• Session boundary issues - Fail to separate distinct conversations

Multi-Turn Reasoning

• Multi-turn reasoning breaks - Can't follow complex, multi-step conversations

• Follow-up question handling - Don't understand context-dependent queries

• Intent drift - Lose track of original user goal

Consistency & Reliability

• Persona drift - Tone or behavior changes mid-conversation

• Tool selection errors - Choose wrong tool or misuse tools

• Inconsistent behavior - Same input yields different outputs

Solutions in This Section

Browse these guides to improve agent behavior:

• Strategy Selection Failures

• Redwood vs Cedar Decision

• Context Assembly Logic Issues

• Agent Memory Corruption

• Conversational Context Loss

• Multi-Turn Reasoning Breaks

• Agent Persona Drift

• Tool Selection Errors

Agent Architecture Patterns

Different architectures for different needs:

1. Simple ReAct Agent

Pattern: Reason → Act → Observe → Repeat

User Query
    ↓
Agent Reasoning: "I need to search for X"
    ↓
Action: Vector Search
    ↓
Observation: Retrieved results
    ↓
Agent Reasoning: "Now I can answer"
    ↓
Generate Response

Strengths:

• Simple to implement

• Transparent reasoning

• Easy to debug

Weaknesses:

• Can get stuck in loops

• Limited planning capability

• No long-term memory

Best for: Single-turn queries, simple tool use

2. Strategy Router

Pattern: Classify query → Route to specialized strategy

User Query
    ↓
Query Classification
    ├─ Factual? → Dense retrieval (Redwood)
    ├─ Technical? → Sparse retrieval (Cedar)
    ├─ Multi-step? → Chain-of-thought reasoning
    └─ Conversational? → Context-aware generation

Strengths:

• Optimized approach per query type

• Better accuracy than one-size-fits-all

• Leverages specialized strategies

Weaknesses:

• Classification can fail

• More complex to maintain

• Need to tune routing logic

Best for: Diverse query types, production systems

3. Multi-Agent System

Pattern: Specialized agents collaborate

User Query
    ↓
Coordinator Agent
    ├─ Retrieval Agent: Find information
    ├─ Reasoning Agent: Answer complex questions
    ├─ Validation Agent: Check answer quality
    └─ Citation Agent: Add source references
    ↓
Synthesized Response

Strengths:

• Specialized expertise per agent

• Modular and maintainable

• High quality through collaboration

Weaknesses:

• High latency (sequential agents)

• Increased cost (multiple LLM calls)

• Complex orchestration

Best for: High-stakes applications, complex workflows

4. Memory-Augmented Agent

Pattern: Short-term + long-term memory

User Query
    ↓
Short-Term Memory: Recent conversation
Long-Term Memory: User preferences, history
    ↓
Context-Aware Processing
    ↓
Update Memory
    ↓
Generate Response

Strengths:

• Personalized interactions

• Conversational coherence

• Learns from interactions

Weaknesses:

• Memory management complexity

• Privacy considerations

• Risk of memory contamination

Best for: Conversational assistants, personalized agents

Best Practices

Strategy Selection

1. Query classification - Categorize before processing

   • Factual vs conversational

   • Simple vs complex

   • Follow-up vs new topic

2. Strategy routing - Map query types to strategies

   Factual question → Dense retrieval + grounded generation
   Technical query → Sparse retrieval + code-aware generation
   Opinion/advice → Context-aware reasoning
   Follow-up → Conversational context + retrieval

3. Fallback strategies - Handle edge cases gracefully

   • If dense retrieval fails → Try sparse retrieval

   • If no context found → Acknowledge limitation

   • If ambiguous query → Ask for clarification

4. A/B testing - Compare strategies empirically

   • Measure accuracy, latency, user satisfaction

   • Iterate based on data, not assumptions

Memory Management

1. Session isolation - Keep conversations separate

   • Unique session IDs

   • Clear session boundaries

   • Flush memory on session end

2. Context window management - Stay within limits

   Priority order:
   1. System prompt (instructions, persona)
   2. Short-term memory (recent conversation)
   3. Retrieved context (relevant documents)
   4. Long-term memory (user preferences)

3. Memory summarization - Compress history

   • Summarize old conversation turns

   • Keep recent turns in detail

   • Preserve key facts and decisions

4. Memory validation - Prevent contamination

   • Verify memory matches user

   • Detect contradictions

   • Clear corrupted memory

Multi-Turn Conversations

1. Context tracking - Maintain conversation state

   Turn 1: "What is RAG?"
   Turn 2: "How does it work?" (it = RAG from Turn 1)
   Turn 3: "Show me an example" (of RAG from Turn 1)

2. Coreference resolution - Understand references

   • Pronouns: "it", "that", "them"

   • Implicit references: "another one", "the same thing"

   • Temporal: "earlier", "before", "later"

3. Intent preservation - Remember the goal

   User: "I need to set up authentication"
   Agent: "Here's how..."
   User: "What about OAuth?" (still about authentication)
   Agent: (Maintain authentication context)

4. Natural hand-offs - Manage topic changes

   • Detect topic shifts

   • Acknowledge transitions

   • Start fresh context when appropriate

Persona & Tone

1. Define persona clearly in system prompt

   You are a helpful, technical support agent.
   - Be concise and direct
   - Use technical terminology appropriately
   - Admit when you don't know
   - Always cite sources

2. Maintain consistency - Same tone throughout

   • Formal vs casual

   • Technical vs accessible

   • Authoritative vs collaborative

3. Adapt appropriately - Match user style

   • Mirror formality level

   • Adjust technical depth

   • Balance brevity with completeness

4. Monitor drift - Track persona consistency

   • Evaluate responses for tone

   • Flag unexpected behavior

   • Retrain or adjust prompts

Tool Use

1. Clear tool definitions - Document when and how

   Tool: web_search
   When: User asks about current events or external information
   Input: Search query string
   Output: List of results with URLs and snippets

2. Tool selection logic - Choose right tool

   • Match tool capabilities to query needs

   • Use simplest tool that works

   • Avoid unnecessary tool calls

3. Error handling - Gracefully handle tool failures

   • Retry with modified input

   • Try alternative tool

   • Inform user of limitation

4. Result interpretation - Understand tool outputs

   • Parse structured results correctly

   • Handle empty or error responses

   • Integrate tool results with other context

Agent Evaluation

Measure agent performance across dimensions:

Accuracy Metrics

• Correctness: Is the answer right?

• Completeness: Is all necessary information included?

• Groundedness: Is answer supported by retrieved context?

• Citation quality: Are sources accurate and helpful?

Behavior Metrics

• Strategy appropriateness: Right approach for query type?

• Tool usage: Correct tools called with right parameters?

• Conversation coherence: Does multi-turn make sense?

• Persona consistency: Tone and style consistent?

Robustness Metrics

• Edge case handling: Performance on unusual queries

• Error recovery: Graceful degradation when things fail

• Adversarial resistance: Response to prompt injection attempts

• Ambiguity handling: Asks clarifying questions appropriately

User Experience Metrics

• Satisfaction: User ratings and feedback

• Task completion: Did user achieve their goal?

• Engagement: Multi-turn usage, follow-ups

• Trust: Do users return? Do they act on advice?

Debugging Agent Issues

Debugging Strategy Failures

1. Log strategy decisions - Record what strategy was chosen and why

2. Review query classification - Was query type identified correctly?

3. Compare strategies - Would alternative strategy have worked better?

4. Adjust routing logic - Update classification or strategy mapping

Debugging Memory Issues

1. Inspect memory state - What's in short-term and long-term memory?

2. Check session isolation - Is memory bleeding across sessions?

3. Review memory updates - What triggered memory changes?

4. Validate memory content - Does memory match actual conversation?

Debugging Multi-Turn Breaks

1. Trace conversation history - Review all turns leading to failure

2. Check context window - Was critical information pushed out?

3. Examine coreference - Were references resolved correctly?

4. Test in isolation - Does problematic turn work standalone?

Debugging Persona Drift

1. Compare responses - Look for tone or style inconsistency

2. Review system prompt - Is persona defined clearly enough?

3. Check context contamination - Is retrieved content influencing tone?

4. Test prompt variations - Try strengthening persona instructions

Advanced Agent Techniques

Chain-of-Thought Reasoning

Make reasoning explicit:

Query: "Which product is best for a small team that needs security?"

Agent thinking:
1. Key requirements: small team, security priority
2. Relevant factors: team size, security features, price
3. Retrieved products: A (enterprise, expensive), B (startup, secure), C (basic)
4. Analysis: B best matches small team + security needs
5. Confidence: High (clear match)

Response: "For a small team prioritizing security, I recommend Product B..."

Self-Correction

Agent validates and corrects own outputs:

1. Generate initial answer
2. Retrieve additional context
3. Check answer against new context
4. If inconsistency found → Revise answer
5. If confident → Return answer

Uncertainty Quantification

Express confidence explicitly:

High confidence: "Based on 5 sources, the answer is X"
Medium confidence: "According to 2 sources, the answer appears to be X"
Low confidence: "I found limited information suggesting X, but I'm not certain"
No confidence: "I don't have enough information to answer this reliably"

Meta-Learning

Agent learns from interactions:

• Track which strategies work for which query types

• Learn user preferences and adapt

• Identify common failure patterns

• Continuously improve routing and behavior

Quick Diagnostics

Signs your agent behavior needs work:

• ✗ Uses wrong retrieval strategy for query types

• ✗ Forgets information from earlier in conversation

• ✗ Mixes up information from different users

• ✗ Can't handle "what about X?" follow-up questions

• ✗ Tone and formality inconsistent across responses

• ✗ Calls tools incorrectly or unnecessarily

• ✗ Repeats information already provided

• ✗ Gets stuck in reasoning loops

Signs your agent is working well:

• ✓ Intelligently routes queries to appropriate strategies

• ✓ Maintains coherent multi-turn conversations

• ✓ Remembers and builds on context appropriately

• ✓ Consistent persona and tone

• ✓ Uses tools correctly and only when needed

• ✓ Handles ambiguity and edge cases gracefully

• ✓ Acknowledges limitations honestly

• ✓ Natural, helpful interactions

Bottom line: Agents are the "brain" of your RAG system. Strategy, memory, reasoning, and consistency are what separate sophisticated AI assistants from simple chatbots. Invest in agent design, test thoroughly, and monitor behavior continuously.