Optimizing an Ollama (Qwen:2.5) AI Agent: Fixing Search Aggregation, Context Bleed, and Query Extraction

I am building a domain-specific AI agent powered by Ollama (using the qwen:2.5 model). For data retrieval, the agent utilizes multiple search APIs: DuckDuckGo Search (DDGS), Tavily, Serper, and Google Places. To optimize performance and reduce API costs, I am using Qdrant DB to cache responses and prevent redundant API calls for identical prompts. However, I am currently facing three critical architectural challenges: Search Merging & Comparison: I want the agent to query all four search services simultaneously, aggregate the results, and intelligently compare or synthesize them into the best possible answer. Currently, I am struggling to implement this multi-source comparison logic. Context Bleed / Hallucination: The agent occasionally hallucinates by returning answers relevant to the previous user prompt instead of the current one. It seems to be mixing up past and present contexts. Poor Search Query Formulation: The agent often tries to search using the raw, full text of the user prompt rather than extracting the core intent. I need a reliable way to make the agent more intelligent so it can isolate specific, relevant keywords or statements from the prompt and use only those for the search queries. Any advice, architectural patterns, or code examples to help resolve these issues would be highly appreciated! submitted by /u/Glad-Finance4354 [link] [comments]

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Source: r/ollama | 2026-07-24