Architectural Innovations
Deep deconstruction of production-grade agentic frameworks, post-training optimization pipelines, and custom integration protocols.
LiveKit A2A Relay — Real-Time Audio Bridge
An ultra-low latency bridge that seamlessly connects a LiveKit WebRTC room to any Agent-to-Agent (A2A) compliant HTTP/SSE endpoint. This crate abstracts away the complexities of real-time audio synchronization, allowing developers to focus on building intelligent AI agents while LiveKit handles high-performance WebRTC transport.
Build Requirements
Because webrtc-sys compiles native WebRTC C++ code and relies on code-generation via bindgen, it requires cmake and Clang / LLVM Headers in the path.
# Set environment variable pointing to Clang include dir if crypt/webrtc compilation fails:
export BINDGEN_EXTRA_CLANG_ARGS="-I/usr/lib/llvm-20/lib/clang/20/include" Teach Gemma 3 to Reason — GRPO Optimization
Developed for the Google Tunix Hackathon (*Kaggle Bronze* winning entry), this post-training pipeline focuses on teaching the Gemma 3 foundation model to reason autonomously. Using a System 2 cognitive architecture (enforced via a custom Plan-Evaluate-Execute XML markup cycle), it significantly decreases hallucination rates.
To bypass TPU-only constraints in the original Tunix framework, I engineered a GPU-unlock translation layer. Using **Group Relative Policy Optimization (GRPO)**, it creates a set of \(G\) completions per prompt, scores them with a reward verifier, and calculates relative advantages—avoiding the memory overhead of a separate critic model:
The relative advantage \(A_i\) is then applied to policy updates using importance sampling ratios, clipped to prevent excessive updates, and penalized with a Kullback-Leibler (KL) divergence term to prevent model drift from the reference policy. This system was orchestrated on a Ray GPU cluster utilizing Unsloth for high-speed 16-bit quantized base weights.
[RLVR] Programmatic Reward Verifier (Python snippet)
def evaluate_reasoning_reward(completion_text: str, expected_answer: str) -> float:
"""
RLVR verifier enforcing XML tag structures and mathematical accuracy.
"""
import re
reward = 0.0
# 1. Enforce strict formatting tags
think_match = re.search(r"<think>(.*?)</think>", completion_text, re.DOTALL)
answer_match = re.search(r"<answer>(.*?)</answer>", completion_text, re.DOTALL)
if think_match and answer_match:
reward += 0.2 # Formatting compliance reward
# 2. Extract and verify answer
extracted_ans = answer_match.group(1).strip()
if extracted_ans == expected_answer:
reward += 1.0 # Accuracy reward
else:
reward -= 0.3 # Incorrect answer penalty
else:
reward -= 0.5 # Strict syntax penalty
# 3. Penalize repetition loop anomalies
if len(re.findall(r"(\b\w+\b)(?=.*\1)", completion_text)) > 20:
reward -= 0.4
return float(reward) MedSwarm — Edge Multi-Agent Diagnostic Network
MedSwarm is designed as a localized multi-agent diagnostic framework executing on edge compute in low-connectivity areas.
Utilizing cyclical state graphs in LangGraph, the system models diagnostic queries as state transitions over a shared AgentState payload.
[LangGraph Schema] ReAct & Debate Cycles
1. Generator Node: Translates patient symptoms into a differential diagnosis trail using local vector indexes (RAG).
2. Critic Node: Implements persona-based adversarial debating, auditing clinical justifications, checking drug contraindications, and flagging diagnostic hallucinations.
3. Adjudicator Edge: Loops back if arguments fail schema checks, routing to the Generator for iterative path updates, or compiling the final treatment output.
By partitioning complex operations into specialized nodes rather than a single monolithic context prompt, MedSwarm reduces hallucination rates under resource-constrained clinical settings.
Standardizing Tool Access via Model Context Protocol
To replace brittle, ad-hoc API orchestrations, I integrated the Model Context Protocol (MCP). MCP is an open standard that decouples front-facing LLM hosts from back-end utility servers using JSON-RPC transports.
| MCP Layer | System Responsibility | Security Boundaries |
|---|---|---|
| MCP Host | Client process initiating LLMs (e.g. voice pipeline runner). | Manages API tokens, coordinates tool-call approvals, and maintains safety filters. |
| MCP Client | Stateful router matching JSON-RPC schema calls. | Validates signatures, serializes parameters, and monitors execution timeouts. |
| MCP Server | Exposes standardized resources and execution tools. | Strict schema validation (Pydantic), sandboxed tool execution, and local-only filesystem bounds. |
[FastMCP] Declaring Tools and Resources
from mcp.server.fastmcp import FastMCP
# Initialize MCP Server for portfolio data lookup
app = FastMCP("PortfolioMetadataServer")
@app.tool()
def search_innovations(category: str) -> str:
"""
Exposes query lookup for candidate's architectural achievements.
"""
data = {
"alignment": "GRPO implementation on Gemma 3 LLM.",
"systems": "Go P2P connection channels & C++ ECC mTLS firmware."
}
return data.get(category.lower(), "Innovation category not indexed.")
@app.resource("resource://resume/experience")
def get_experience_json() -> str:
"""
Returns structured JSON data of candidate career history.
"""
return """
{
"candidate": "Jayaprakash M",
"active_role": "Senior Development Engineer",
"company": "Truminds Software Systems"
}
""" ProdAIve — Real-Time Kiln Optimization & Data Science Agent
ProdAIve is a transformative AI optimization system designed to bring agentic automation to cement manufacturing kiln operations. It integrates Google's Gemini models for guided troubleshooting and natural language UI orchestration.
The system leverages an autonomous LangChain Data Science Agent to parse kiln sensor parameters, query Google BigQuery logs, and trigger predictive models deployed on Vertex AI endpoints to anticipate temperature anomalies and optimize thermal efficiency.
MedTrust — Decentralized Healthcare Agent Swarm
MedTrust was developed for the AWS AI for Bharat Hackathon 2026. It is a production-ready, serverless agentic ecosystem that bridges patients, clinical practitioners, and government bodies (CDSCO, ICMR, NPPA data).
Architected as an Agent-to-Agent (A2A) monorepo, it orchestrates four distinct roles:
patient-agent (React Native PWA), doctor-agent (clinical portal), gov-agent (public health analytics), and cloud-agent (an AWS Bedrock-powered Claude 3 Haiku relay executing secure, cryptographic intent schemas).