# 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](/rag-scenarios-and-solutions/agent/strategy-selection.md) * [Redwood vs Cedar Decision](/rag-scenarios-and-solutions/agent/strategy-decision.md) * [Context Assembly Logic Issues](/rag-scenarios-and-solutions/agent/context-assembly.md) * [Agent Memory Corruption](/rag-scenarios-and-solutions/agent/memory-corruption.md) * [Conversational Context Loss](/rag-scenarios-and-solutions/agent/conversation-loss.md) * [Multi-Turn Reasoning Breaks](/rag-scenarios-and-solutions/agent/multi-turn-breaks.md) * [Agent Persona Drift](/rag-scenarios-and-solutions/agent/persona-drift.md) * [Tool Selection Errors](/rag-scenarios-and-solutions/agent/tool-selection.md) ## 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://help.twig.so/rag-scenarios-and-solutions/agent.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.