AI Agent

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Agentic Retrieval-Augmented-Generation (RAG)

My work on Agentic RAG significantly enhances Large Language Model (LLM) performance for complex information-seeking. This project integrates intelligent AI agents into the RAG pipeline, yielding remarkably more accurate, robust, and contextually rich responses than traditional RAG.

Brief Technique & Impact

I developed an AI agent (using the smolagent package) capable of dynamic decision-making, iterative query reformulation, and intelligent document evaluation. A key contribution is an optimized parallel processing pipeline for efficient FAISS-based vector database creation from technical documentation. This framework fundamentally improves LLM output grounding through advanced reasoning and self-correction.

Performance Highlights

Evaluated on a technical Q&A dataset, Agentic RAG consistently demonstrated superior accuracy across various LLMs compared to both Standard RAG and standalone LLM performance:

More details can be found in the project repository on GitHub.

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Agentic RAG with Colocated vLLM Inference

This project evaluates Agentic RAG, traditional RAG, and standalone LLM systems on complex technical queries. All inference is successfully moved from remote APIs to a GPU cluster with colocated vLLM serving, ensuring zero-egress data sovereignty.

Brief Technique & Impact

The framework introduces a dynamic agent-based approach for iterative query refinement using smolagents. It runs a three-phase hybrid pipeline combining offline batching and an asynchronous server. This design maximizes GPU utilization and enables concurrent multi-step reasoning, drastically reducing latency compared to traditional API-limited regimes to deliver highly efficient, robust answers.

Performance Highlights

Deploying concurrent Agentic RAG queries on a local vLLM server collapses latency by an order of magnitude. Concurrency and batching transform API-limited pipelines into highly practical, compute-efficient, high-throughput local systems.

Note: Lower inference times indicate superior system efficiency.

More details can be found in the project repository on GitHub.

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AI Agent: Tool-Augmented Reasoning on GAIA Benchmark

I developed a tool-augmented AI code agent using the smolagents framework to tackle complex, agent-evaluating questions from the GAIA benchmark.
This system achieved a 40% correct answer rate—substantially outperforming GPT-4, which reached 14.4% under the same conditions.

Note: This project is currently under active development to further improve accuracy and generalization.