From: aidotengineer
The year 2025 is considered the “perfect storm” and “AI agent moment” due to significant advancements in the field [03:38:00]. Progress in AI has been exponential, with models becoming more aggressive, impressive, and widespread beyond just major players like OpenAI and Anthropic [02:20:00]. Key developments include:
- Reasoning models (e.g., OpenAI’s O1 and O3, DeepSeek’s R1, Grok’s latest) outperforming human ability and showcasing novel capabilities [03:47:00].
- The rise of test-time compute, increasing model performance [04:04:00].
- Enhanced engineering and hardware optimizations, leading to more compute-efficient models and cheaper inference [04:12:00].
- A closing gap between open-source (e.g., DeepSeek, LLaMA) and closed-source models [04:27:00].
- Billions in infrastructure investments, such as the US Stargate project, France’s AI initiative, and Japan’s efforts with Nvidia [04:34:00].
Defining AI Agents
For the purpose of this discussion, an AI agent is defined as a fully autonomous system where large language models (LLMs) direct their own actions [05:12:00].
Challenges in AI Agent Development
Despite the rapid advancements, AI agents are not yet consistently effective [05:00:00]. The perception of a “perfect storm” for AI agents has yet to manifest in tangible results [05:03:00]. A significant hurdle is the accumulation of “tiny cumulative errors” rather than just obvious hallucinations or fabrications [06:54:00].
Consider the example of an AI agent attempting to book a complex flight:
- Decision Error: The agent might choose the wrong fact, such as booking a flight to “San Francisco, Peru” instead of “San Francisco, California” [07:10:00].
- Implementation Error: This occurs when there’s wrong access or integration, like encountering a CAPTCHA or being locked out of a critical database, preventing the agent from completing its task [07:25:00].
- Heuristic Error: The agent might apply the wrong criteria, failing to acknowledge best practices like allowing sufficient time for travel to a specific airport like JFK based on origin and traffic conditions [07:44:00].
- Taste Error: The agent fails to account for personal preferences not explicitly stated in the prompt, such as booking a flight on a specific aircraft type that the user wishes to avoid [08:03:00].
These errors compound within complex systems. An agent with 99% accuracy can drop to 60% accuracy after 50 consecutive steps, highlighting how even minor errors amplify in multi-step tasks within a multi-agent system [08:49:00].
Strategies for Building Effective AI Agents
To optimize complex agents and consistently make the right decisions, several strategies can mitigate cumulative errors:
Data Curation
Ensuring an AI agent has the necessary, high-quality information is crucial [10:09:00]. Data is often messy, unstructured, and siloed, encompassing web, text, design, image, video, audio, sensor data, and even agent-generated data [10:11:00]. Key aspects include:
- Curating proprietary data generated by the AI agent itself [10:32:00].
- Designing an agent data flywheel from day one, so user interactions automatically improve the product in real-time and at scale [10:49:00].
Importance of Evals
Collecting and measuring a model’s responses to determine the correct answer is vital [11:22:00]. While straightforward in verifiable domains like math and science, evaluating non-verifiable systems (e.g., subjective preferences) is more complex [11:33:00]. It requires collecting human preferences and building evaluations that are truly personal [12:27:00]. Sometimes, the best evaluation is simply trying the agent yourself and relying on “vibes” based on individual needs [12:34:00].
Scaffolding Systems
Building robust scaffolding ensures that a single error does not cascade throughout the entire agentic system or production infrastructure [12:45:00]. This can involve:
- Creating a complex compound system that integrates different components [13:08:00].
- Incorporating human intervention points (human-in-the-loop) for reasoning models [13:12:00].
- Adapting scaffolds to enable stronger agents that can self-heal, correct their own paths, or break execution when uncertain [13:18:00].
User Experience (UX)
UX is paramount, as AI apps often use the same underlying models, making foundation models a rapidly depreciating asset class [13:53:00]. A strong UX differentiates products by reimagining experiences, deeply understanding user workflows, and promoting elegant human-machine collaboration [14:00:00]. This includes features like asking clarifying questions, predicting user next steps, and seamless integration with legacy systems to create real ROI [14:13:00]. Companies with proprietary data sources and deep knowledge of user workflows (e.g., in robotics, hardware, defense, manufacturing, life sciences) are well-positioned to create magical experiences [14:55:00].
Building Multimodally
Moving beyond chatbots, developing multimodal AI agents can create a 10x more personalized user experience [15:22:00]. Making AI more human means adding senses like eyes, ears, nose (e.g., digitizing the sense of smell), and touch (for embodiment in robotics) [15:40:00]. Incorporating “memories” to make AI truly personal and deeply understand users can reframe what perfection means to a human, allowing visionary products to exceed expectations even if agents are inconsistent [16:07:00].
In conclusion, while 2025 presents a “perfect storm” for AI agents, achieving true functionality requires addressing cumulative errors through meticulous data curation, robust evaluations, resilient scaffolding systems, exceptional user experience design, and multimodal development [16:51:00].