From: aidotengineer
Developing AI agents presents various technical challenges, particularly in an era characterized by a clear shift towards bigger, smarter models and the widespread availability of intelligence [02:09:00]. This evolving landscape creates both opportunities and challenges [02:43:00].
Key Challenges in Creating Effective AI Agents
Building effective AI agents requires dedicated effort in several key areas [03:06:00]:
Scoping Tasks for Evaluation
One of the most significant challenges is accurately scoping tasks for evaluation [08:01:00]. While it’s easy to quickly build demos, it is much harder to scope and evaluate what is actually occurring [08:05:00]. Defining clear “jobs to be done” and understanding step-by-step human processes are crucial [08:33:00].
It is particularly difficult to make an agent’s performance measurable and verifiable at each step over time [08:54:00]. This is a common pain point where a demo might look functional but is hard to verify and improve continually [09:01:00].
“I can’t stress this enough… start by thinking deeply about your eval. The number of mistakes we made by not thinking about eval first is frustrating” [09:31:00]
This “fuzzy stochastic world” of AI agents necessitates robust evaluation methods, including offline, online, and living evaluations [09:48:00]. End-to-end measurements and proper instrumentation are vital to gather human feedback and continuously improve the test set [09:57:00].
Building the Right Team
Assembling a team ready to move fast and handle ambiguity is essential [08:09:00]. It’s not necessary to have a large number of machine learning experts, as they are scarce [10:14:00]. Instead, a team can be seeded with one or two ML experts and then filled with optimistic generalists who are skilled at writing code and willing to experiment quickly [10:20:00].
Crucially, teammates should be excited about being AI-augmented themselves and possess an explorer mindset, eager to learn in a rapidly changing field [10:38:00].
Adapting User Experience (UX)
The traditional user experience (UX) is changing dramatically with AI agents [08:15:00]. UX and front-end development are more important than often realized, especially for human-AI collaboration [10:28:00]. As AI agents move from experimental to mainstream, new UX patterns are emerging, and developers must be comfortable with this shift [11:18:00]. The speaker prefers agents that behave more like human teammates rather than requiring numerous new pages or buttons [11:28:00].
Ensuring Observability
Observability is critical and should not be an afterthought in AI agent development [08:21:00]. AI agents often involve complex workflows, making situational awareness vital for debugging problems [11:42:00].
However, observability can quickly become messy with agents [12:26:00]. An agent’s multi-step calls can be incredibly complex, involving hundreds of decisions, tool usages, and loops [12:28:00]. Simply reviewing a list of these interactions makes it nearly impossible to understand what is happening [12:41:00]. Visualizing these complex workflows, such as through an agent graph, is essential for human-readable debugging [12:46:00].
Human-AI Collaboration and Trust
A significant challenge lies in figuring out the expected level of collaboration between humans and AI agents [04:51:00]. While agents are designed to act like humans, there’s a need for humans to verify their actions, oversee their processes, and learn from them [04:56:00]. This continuous verification helps earn trust over time, allowing users to see the agent’s reasoning, findings, and decision-making steps [05:03:00].
The “Bitter Lesson” of AI Development
A key insight, akin to the “bitter lesson” in application development, is that general methods leveraging off-the-shelf models are ultimately the most effective [13:14:00]. Extensive fine-tuning for specific projects or tasks can quickly become outdated as new, more powerful models are released by major AI developers [13:26:00]. Therefore, it’s crucial for development teams to be adaptable and able to easily switch between different models [13:45:00].
Designing for Agent Users
Looking ahead, a significant challenge involves designing products not just for human users but for AI agents themselves [14:01:00]. There’s a strong possibility that agents will surpass humans as primary users of Software-as-a-Service (SaaS) products within the next five years [14:07:00]. This means developers must consider how agents will consume their product, providing appropriate context and API information that agents would utilize more effectively than humans [14:21:00].