Speakers: Alex (Cloud Relations at Anthropic), Eric (Research team at Anthropic), Barry (Applied AI team)
Topic: Understanding agents in AI and their applications.
Understanding Agents
Definition: An agent is more than a single LLM call. It involves letting the LLM decide how many times to run and continuing until a resolution is found.
Examples: Customer support, code iteration.
Distinction:
Agent: Autonomous, runs until completion without a predefined path.
Workflow: Fixed steps, pre-orchestrated like a series of prompts (prompt A to prompt B to prompt C).
Development and Implementation
Evolution: As models and AI tools improve, agents become more prevalent and capable.
Patterns: Two distinct patterns observed:
Workflows: Pre-orchestrated by code.
Agents: Autonomous, complex decision-making.
Agent vs. Workflow in Practice
Workflow Example: Sequential prompts with a fixed path.
Agent Example: Open-ended with multiple tools, continues until a solution is reached.
Developer Tip: Consider the model's perspective to create effective prompts and tool descriptions.
Challenges and Observations
Agent Design: Requires understanding the model's context and capabilities.
Verification: Essential for coding tasks, not as prevalent or straightforward in other applications.
Potential and Future of Agents
Current Use Cases:
Coding agents: Potential through verifiable tasks like unit tests.
Search: Agent-led search can be precise by trading off precision for recall.
Overhyped vs. Underhyped:
Overhyped: Agents for consumers - difficult to specify preferences without human intervention.
Underhyped: Small time-saving tasks that can be scaled significantly.
Future Predictions
2025 and Beyond:
Multi-Agent Environments: Potential interactions between agents (e.g., playing games like Werewolf).
Business Adoption: Automating repetitive tasks, scaling operations.
Consumer Context: Need for models to learn user preferences over time.
Advice for Developers
Measurable Results: Ensure there is a way to measure and feedback on your AI applications.
Start Simple: Build complexity as you go based on measurable outcomes.
Future-Proofing: Build systems that improve as AI models get smarter.
Conclusion
Building effective agents involves understanding both the potential and limitations of AI models, focusing on verifiable and scalable tasks, and preparing for future advancements in AI capabilities.