Demonstrating a practical AI agent workflow highlights essential features and behaviors to observe during live implementation.

Leveraging Retrieval-Augmented Generation (RAG) and advanced AI assistant features to enhance conversational agent capabilities and developer workflow...Show more

Using a modular architecture that allows updating specialist prompts to quickly adapt agents to different subject domains with minimal changes.

Implement dynamic abstraction layers in agent architectures to enable flexible adaptation and modular enhancements.

Schedule regular code walkthroughs during demos to discuss implementation details and improve team understanding.

Leverage a multi-agent ‘chain of debate’ architecture to structure complex use-case discussions and improve decision rationale.

Maintain a shared space for uploading demo recordings to keep stakeholders aligned and accessible for asynchronous review.

Set up a build pipeline on GitHub to enable continuous integration for automated testing and deployment.

Adopt multiple feature branches to incrementally add components like a Bayesian module for modular and parallel development.

Generated most of the system's architecture in a single attempt and only required iterative debugging to fix critical errors, demonstrating efficient ...Show more

The entire diagnostic workflow was generated from a single initial prompt with minimal manual intervention, highlighting prompt engineering's orchestr...Show more

Implementing chain-of-debate orchestration logic structures agent interactions sequentially to handle non-deterministic tasks effectively.

The system auto-generates agent interface-based classes for five specialist roles to enable modular, role-specific logic within a multi-agent orchestr...Show more

Pressing Shift+Tab cycles between 'Ask' and 'Agent' modes in the UI, mirroring Cursor's dual-mode concept to streamline developer workflows.

You can leverage LangGraph for agent design and connect it with LangSmith for monitoring and analysis.

The evaluation uses the SD Bench dataset, aligning with academic research methodologies rather than product-focused metrics.

The discussion focused on running tests and leveraging available answer sets to validate model outputs.

Clarification was sought on what 'evaluations' meant, specifically regarding which metrics or benchmarks were used to assess the model's performance.

Physicians intentionally compared the AI system's performance to their own clinical reasoning to benchmark the model against expert human judgment.

Implement a structured evaluation framework that subjects AI agents to the most difficult medical diagnostic scenarios to assess accuracy and robustne...Show more