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Jared Zoneraich, building in residence at Cognition, advises companies building AI agents to focus on leveraging the inherent progress of underlying models rather than relying on complex prompt engineering "hacks." He suggests that as models improve, the need for intricate prompt instructions will diminish, and companies should build their roadmaps accordingly.
Jared Zoneraich highlights that 'tool engineering' will become a crucial skill in developing AI agents. He posits that the focus should shift to identifying and providing the right tools for models, with the assumption that the models themselves will become adept at figuring out how to use them.
Jared Zoneraich discusses what makes a company's AI agent development defensible, suggesting that proprietary data is one aspect. He notes that many companies are currently 'AI wrapper companies' primarily focused on prompts and skills, but true defensibility may lie elsewhere.
Jared Zoneraich describes Cognition's approach to introducing AI agents to enterprises as a 'forward deploy motion.' This strategy prioritizes demonstrating the magic of agents by using them to solve actual customer problems, thereby proving their value by taking tickets off client boards.
Jess Yan from Anthropic explains the evolution of AI agents from simple prompt-response loops to autonomous, self-discovering actors capable of long-running tasks. These agents can now interact with third-party systems and sensitive data, requiring enhanced permissioning and observability.
Jess Yan defines an AI agent as an evolution from simple prompting loops to systems with access to tools, memory, and third-party systems, necessitating components like the model, system prompt, and a 'harness' for scaffolding and control.
Jess Yan argues that maximum performance for AI models is achieved when the model and its harness are developed together. She notes that models are always tested in conjunction with specific harnesses, creating a symbiotic relationship.
Anthropic's Claude Managed Agents are designed as a pre-built harness and infrastructure to enable complex tasks at scale with high return on effort. The goal is to provide easy-to-stack primitives and flexible APIs for delegating work that might otherwise take days or weeks.
Jess Yan demonstrates an Anthropic Managed Agent configured for analysis on a fictitious grocery store dataset. The agent utilized a pre-built toolset, interacted with its file system, and produced three HTML files summarizing the analysis results after being given specific data schemas and task guidance.
Jess Yan explains that Anthropic agents can be connected to third-party systems, such as internal databases, through MCP (Messaging and Communication Protocol). MCP provides a standardized way to communicate with external services and includes an authentication layer for secure access.
Jess Yan contrasts basic prompting loops with more advanced self-recovering agents. She highlights that synchronous prompting loops are highly dependent on prior successful requests and struggle to pivot or handle errors gracefully, whereas self-recovering agents can manage errors and steer back on course.
Jess Yan explains that for AI agents to produce predictable outputs, especially when handling complex tasks, it's beneficial to be prescriptive with initial prompts. This includes providing data schemas and breaking down tasks into discrete segments, which also aids in debugging.