Begin with a high-level dataset overview of total sales value, revenue, and median revenue across sectors to form a narrative before deep-diving into specific opportunities.
Use a bubble chart plotting revenue per employee versus number of employees to identify sectors with balanced scale and efficiency for AI product targeting.
Use the Tableau public cloud publication and the Anthropics dataset in a Jupyter notebook to fully replicate the analytical process described in the discussion.
Adopt a structured discovery process by mapping user tasks, market signals, and AI capabilities to recognize and evaluate new opportunities in agentic application development.
Before standardized protocols like MCP, developers implemented simple agent patterns acting as translators or intermediaries to bridge communication gaps between systems.
Hub-and-spoke swarm patterns become difficult to manage and maintain clarity as agent count grows, requiring strategies to control interaction complexity.
Implementing a supervisor pattern where a supervisor agent orchestrates specialized agents (e.g., research agents) helps organize and manage complex agent interactions.
Swarm architecture enables each agent to access and communicate with every other agent, supporting flexible, dynamic interactions within a multi-agent system.
Open Agent Platform ties all data and performance metrics to individual threads and user accounts to ensure granular accountability and reproducibility.
Langsmith provides interfaces for managing and visually displaying agents within the platform, facilitating easier troubleshooting and oversight of agentic workflows.
The system delegates tasks to specialized agents like the Deep Wiki Agent using tools such as the Read Wiki Structure Tool to streamline complex workflows.
