Sensitive database containing information about clinical patients etc.

(Benmort reports, MIMIC Clinical Database, Pubmed Reports)

We want to pre-process our unstructured medical data to reduce tokens and clean unnecessary info as much as possible.

assigning different models for different tasks (e.g., a fast model for drafting, a powerful model for strategic decisions)

Our planner agent will create a JSON-based SOP for our RAG agentic system, which defines how, when, and what actions to take.

Based on the plan a team of agents goes to work, each querying its own dedicated knowledge store

All the findings from the specialist agents are collected, and consolidated all information and into a formal report

The generated document is run through a rigorous, multi-dimensional evaluation system that produces a 5-point report card.

This high-level agent analyzes the scores and identifies the single biggest weakness (e.g., “The feasibility score is only 0.39, which is too low.”).

Its job is to intelligently re-write the rules (the GuidSOP) the Inner Loop follows. Based on the diagnosis, it proposes 2-3 new, “mutated” SOPs.

Tools that Bridge Agentic Reasoning with Real-World Scientific Data

Medical Sub agents System for Autonomous Scientific Discovery

Multi Agentic based Medical Thinking System

Data Pipeline to Transform Raw Agentic Traces into Specialized, Algorithm-Ready Datasets

We evaluate Finetuned based agentic architecture on three factors, qualitative, quantitative, and performance.

LLM judges by faithfulness, relevance, soundness, and depth.

Quantitative eval measures retrieval precision and recall.

Performance eval tracks latency (time) and cost (tokens) per query.

RL Algorithms for training different sub-agents

Monitoring the training/Inferencing process of ai agents