Research Areas

Building reliable orchestration layers that coordinate AI models, tools, and external services into coherent pipelines. We focus on task routing, dependency resolution, execution guarantees, and composability across heterogeneous environments.

Designing frameworks where multiple AI agents collaborate, delegate, and reason together to solve complex, long-horizon tasks. Our work covers agent communication protocols, role specialization, conflict resolution, and emergent coordination behavior.

Researching and developing foundation models and fine-tuned variants optimized for specific domains and tasks. We study alignment, instruction-following, reasoning quality, and the integration of tool use directly into model behavior.

Creating AI-native workflow systems that can plan, execute, and adapt multi-step business processes with minimal human intervention. We investigate dynamic plan generation, error recovery, and human-in-the-loop integration points.

Advancing memory architectures that allow AI agents to retain context across sessions, accumulate experience, and reason over long-term knowledge. Research spans episodic memory, semantic retrieval, memory compression, and forgetting mechanisms.

Building the platform primitives — runtimes, scheduling, observability, and deployment tooling — that make production AI systems reliable and scalable. We focus on latency, throughput, cost efficiency, and operational visibility at scale.