Agency vs. Control vs. Reliability in Agent Design
24 days ago
- #Customer Support
- #LLM Reliability
- #AI Agents
- High-agency tasks require agents to act competently, reliably, and consistently, especially in high-value use cases like customer support.
- Customer support is challenging due to knowledge gaps, impatient users, and time constraints, contrasting with ideal environments where agents have complete knowledge and forgiving conditions.
- LLMs like Anthropic's 'computer use' and OpenAI's DeepResearch show advancements in high-agency tasks, but real-world applications like Fin face reliability issues.
- Customers expect high reliability and control from agents, especially for sensitive tasks like subscription management, refunds, and context gathering.
- Measuring agent performance involves simulating tasks with predefined outcomes, user prompts, and stopping conditions to assess reliability and consistency.
- The 'pass^k' metric is stricter than 'pass@k', requiring consistent success over multiple repetitions, which is crucial for customer support reliability.
- Agency and reliability are inversely related; high-agency agents often perform inconsistently, especially in complex tasks.
- The 'Give Fin a Task' (GFAT) agent balances agency and control by using step-based instructions, improving reliability for simple and moderate tasks.
- GFAT's composability allows breaking complex tasks into simpler, more reliable steps, enhancing overall performance and customer satisfaction.
- Early benchmarks show GFAT significantly improves reliability, especially for simple and moderate tasks, by constraining agency and emphasizing structured execution.