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3
1
Bangladesh
1 year of experience
I am Md Abu Ammar, a Computer Science and Engineering student from Bangladesh with a strong interest in AI, machine learning, research, and building real-world tech solutions. I enjoy working on innovative projects, especially in education technology, healthcare AI, and data-driven systems. I am passionate about learning, problem-solving, and creating products that can make a meaningful impact. Recently, I have been involved in hackathons and AI-based projects, where I focus on turning ideas into practical solutions.

Sentinel is an autonomous, multi-agent architecture review system designed for regulated and high-stakes enterprise environments. In industries like finance, healthcare, and insurance, deploying new technical workflows requires rigorous scrutiny across multiple domainsβsecurity, compliance, and IT governance. Manual reviews are often massive bottlenecks. Sentinel solves this by orchestrating a team of specialized AI agents through the Band collaboration layer to autonomously debate, audit, and score technical proposals. Built specifically for Track 3 (Regulated & High-Stakes Workflows), Sentinel goes beyond simple linear automation or thin API wrappers by utilizing Band as a true shared interaction layer. The workflow is managed by the Conductor agent, which ingests technical architecture documents and coordinates the room. The Harness agent injects historical company policies and compliance baselines directly into the shared workspace. Operating in parallel, the Adversary aggressively red-teams the architecture for critical security flaws (such as prompt injection vulnerabilities in LLM execution flows), while the Guardian audits for regulatory violations (such as GDPR or SOC2 data handling failures). Because these agents operate within a shared Band chat room, they do not work in silos. Once the specialized audits are complete, the Evaluator reads the room's context to synthesize the distinct flags into a cohesive architectural review. Finally, the RiskScorer processes this evaluation to generate a definitive, quantitative risk matrix and an automated approve/escalate decision payload. By demonstrating real agent-to-agent collaboration, role specialization, complex context exchange, and task handoffs, Sentinel proves that multi-agent systems can handle complex, regulated decision-making safely, transparently, and efficiently.
19 Jun 2026