What I Built
I built Solomon AI, a multi-agent orchestration engine designed to turn high-level user intent into structured, autonomous workflows.
The Problem
Most conversational AI systems stop at generating text or suggestions. Users are still left with the manual work of executing multi-step tasks across different services and environments. Solomon AI bridges this gap by discovering user intent, decomposing complex objectives into sub-tasks, and orchestrating specialized agents to execute real-world actions concurrently.
Architecture & Key Features
- Intent Execution Engine: Deconstructs complex user prompts into actionable multi-step task graphs.
- Agent Orchestration: Manages multi-agent communication, passing context dynamically between specialized execution agents.
- Local & Cloud Hybrid Infrastructure: Runs fast, private local inference using Ollama while seamlessly connecting to cloud APIs when external actions or compute are required.
What I Learned
- Structuring robust failure-handling mechanisms for autonomous agent loops.
- Optimizing prompt context windows and state management across multi-agent workflows.
Partner Technologies
In building Solomon AI, I leveraged key partner technologies to build a scalable and resilient infrastructure:
- GitHub: Served as the central repository for modular codebase organization, issue tracking, and automated testing via GitHub Actions.
- Google Cloud Platform (GCP): Hosted core API microservices and event queues, providing reliable low-latency backend infrastructure to handle incoming execution streams.
Hackathon Experience
I participated in Hack Days Bengaluru! The event was packed with intense energy, high-paced technical discussions, and brilliant developers building across AI, cloud systems, and edge computing.
Building through the night under strict time constraints emphasized the importance of modular system architecture. Engaging with fellow hackers and sharing approaches to agentic workflows was one of the highlights of the experience.
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