Agentic RAG, Multi-Agent and Vision with Reasoning

In 2025, the most advanced systems no longer rely on a single large, general-purpose model. Instead, they use multiple LLMs of different sizes and specialties, each handling a specific task like a team of experts working together on a complex problem.

However, properly evaluating an LLM within its specific role in such an architecture is very challenging, since it needs to be tested directly in the context where it is used.

We are going to build three distinct, production-grade AI pipelines and use them as testbeds to measure how different models perform in their assigned roles. Our evaluation framework is built on three core principles:

1. Role-Specific Testing: We evaluate models based on the specific task they are assigned within the pipeline, whether it’s a fast router, a deep reasoner, or a precise judge.
2. Real-World Scenarios: Instead of stopping at abstract tests, we are going to run our models through complete, end-to-end workflows to see how they perform under realistic conditions.
3. Holistic Measurement: We are going to measure everything that matters, from operational metrics like cost and latency to qualitative scores like faithfulness, relevance, and scientific validity.

All the code (theory + 3 notebooks) is available in my GitHub repository:

GitHub – FareedKhan-dev/best-llm-finder-pipeline: Agentic RAG, Multi-Agent Systems, and Vision…

Agentic RAG, Multi-Agent Systems, and Vision Reasoning are three pipelines to find the perfect LLM …

github.com

Our codebase is organized as follows:

best-llm-finder-pipeline/
├── 01_agentic_RAG.ipynb        # Agentic RAG
├── 02_multi_agent_system.ipynb # Multi-agent system
├── 03_vision_reasoning.ipynb   # Vision reasoning
├── README.md                   # Project docs
└── requirements.txt            # Dependencies

Our evaluation strategy for choosing the right LLM for each…

Analyzing Our Findings

Across three distinct and challenging use cases, we have moved beyond abstract benchmarks and evaluated open-source LLMs. Let’s take a look at our findings.

• For Broad, High-Throughput Tasks (Routing, Ideation): Smaller, faster models like Qwen/Qwen3-4B-fast and Llama-3.1-8B-Instruct proved to be the champions. They are incredibly cost-effective and their speed is essential for tasks that require processing large amounts of data or generating many parallel outputs. Their lower reasoning power is not a major drawback in these roles.
• For Focused, Logical Tasks (Safety, Refinement): Mid-size models like Qwen/Qwen3-14B hit the sweet spot. They possess strong instruction-following and tool-use capabilities, making them perfect for specialized, logical tasks that don’t require the deep understanding of a flagship model.
• For Deep, High-Stakes Reasoning (Synthesis, Critique, Evaluation): This is where the largest models, like Qwen/Qwen3-235B-A22B, Llama-3.3-70B-Instruct, and DeepSeek-V3, are indispensable. Their superior reasoning, synthesis, and evaluation capabilities justify their higher cost and latency when the quality of the final output is paramount.
• For Multimodal Perception (OCR): Specialized vision-capable models like gemma-3-27b-it are a necessity. This demonstrates that for certain tasks, the key selection criterion isn’t just size, but the fundamental modality the model was trained on.

Key Takeaways

There is a lot of information in this blog, let’s quickly summarize the most important points.

• No single best LLM: The most effective systems combine multiple models, using cheaper ones for scale and expensive ones for precision.
• Role-based selection: Assign models to roles like router, synthesizer, or judge, rather than seeking one model for everything.
• Escalation pattern: Begin with fast, low-cost models to structure or filter problems, then escalate promising cases to stronger models for deeper analysis.
• Automated evaluation: Reliable pipelines require continuous measurement, using top-tier LLMs as judges to score quality and uncover weaknesses.
• Observability: Monitoring cost, latency, and qualitative metrics in a unified view is critical for optimization, debugging, and demonstrating value.

Most teams, when creating a production-ready RAG system on their data, go through many rounds of experimentation and rely on several different components, each requiring its own setup, tuning, and careful handling. These components include…

1. Query Transformations: Rewriting user questions to be more effective for retrieval.
2. Intelligent Routing: Directing a query to the correct data source or a specialized tool.
3. Indexing: Creating a multi-layered knowledge base.
4. Retrieval and Re-ranking: Filtering noise and prioritizing the most relevant context.
5. Self-Correcting Agentic Flows: Building systems that can grade and improve their own work.
6. End-to-End Evaluation: Objectively measuring the performance of the entire pipeline.

and much more …

We will learn and code each part of the RAG ecosystem along with visuals for easier understanding, starting from the basics to advanced techniques.