Agency vs. Control vs. Reliability in Agent Design

Summary

The ability to perform high-agency tasks is important, but it is just as important to ensure that agents can execute tasks competently, reliably, and consistently, when deploying them in high value use cases. Why is customer support such a challenging space? Over the past few months, Large Language Models (LLMs) have significantly advanced. Products like ‘computer use’ from Anthropic and OpenAI, and DeepResearch by OpenAI, demonstrate LLMs’ increasing capability in high-agency tasks. High-agency agents are those where an agent’s actions are primarily self-governed, constrained only by its environment, and a goal. However, most examples of high agency agents operate in ideal environments which provide complete knowledge to the agent, and are ‘patient’ to erroneous or flaky interactions. That is, the agent has access to the complete snapshot of its environment at all times, and the environment is forgiving of its mistakes. This contrasts sharply with customer support agents, like Fin, who generally have knowledge gaps since they are configured by real humans, and interact with humans who may be incoherent, and are often impatient and frustrated. In addition, the agents are highly constrained by how much time they can spend solving a problem, as latency is a core user experience issue. What are the customer’s expectations from agents? Fin has excelled at addressing informational queries using its state of the art RAG framework. This framework has achieved resolution rates in the high 60s for customers with well-developed knowledge bases. However, as Fin resolves more informational queries, we are seeing a rise in demand for it to solve complex problems. Achieving this introduces many requirements, like conversational debugging by gathering personalised context, getting personalised data from external APIs, asking questions, executing business decision logic, and much more. Typical use cases from our customers include: Subscription/order management: renewing, cancelling...