From: aidotengineer
Barry, a speaker and co-author of the blog post “Building Effective Agents”, discusses three core ideas for building effective agents: not building agents for everything, keeping them simple, and thinking like the agents themselves [00:44:00]. These principles are crucial for developing AI agents and agentic workflows, especially when considering the design and optimization of the environments in which they operate.
Understanding Agentic Systems and Environments
The evolution of AI applications has moved from simple features like summarization and classification to more sophisticated workflows that orchestrate multiple model calls [01:07:00]. These workflows are seen as the beginning of agentic systems [01:37:00]. Unlike predefined workflows, agents can determine their own trajectory and act almost autonomously based on feedback from their environment [01:52:00]. As these systems are given more agency, they become more useful and capable, but also incur higher costs, latency, and consequences for errors [02:20:00].
Components of an Agent and Its Environment
Agents are fundamentally conceptualized as models using tools in a loop [05:51:00]. Three critical components define an agent:
- The Environment: This is the system in which the agent operates [06:02:00]. Its design largely depends on the specific use case [07:04:00].
- Tools: These offer an interface for the agent to perform actions and receive feedback within the environment [06:06:00].
- System Prompt: This defines the agent’s goals, constraints, and ideal behavior within the given environment [06:12:00].
Keeping these core components simple is crucial for rapid iteration and achieving a high return on investment (ROI) [06:27:00]. Optimizations, such as trajectory caching to reduce cost or parallelizing tool calls to reduce latency, should be considered only after the basic behaviors of the agent are established [07:33:00].
Thinking Like Your Agent for Better Environment Design
A common pitfall in developing AI agents is designing them from a human perspective, leading to confusion when agents make unexpected mistakes [08:09:00]. To overcome this, it’s recommended to “put yourself in the agent’s context window” [08:20:00].
Agents, despite sophisticated behavior, operate by running inference on a very limited context (typically 10 to 20k tokens) [08:32:00]. Everything the agent knows about the current state of the world is within this context [08:37:00]. It’s vital to ensure this context is sufficient and coherent [08:46:00].
Practical Application: Computer Use Agents
Consider a computer use agent that receives a static screenshot and a poorly written description. When it attempts an action, it’s akin to closing one’s eyes for a few seconds—it doesn’t “see” what’s happening during the tool execution and inference phase [09:34:00]. This “blind execution” is a significant challenge in the agent’s interaction with its environment [09:48:00].
By experiencing a task from the agent’s perspective, developers can identify crucial environmental information that is missing. For a computer use agent, this might include:
- Screen resolution to enable accurate clicks [10:14:00].
- Recommended actions and limitations to provide guardrails and avoid unnecessary exploration [10:19:00].
This exercise helps determine the precise context an agent needs to perform its task effectively within its environment [10:31:00].
Using AI to Optimize Agent Environments
One method to optimize environment context is to use a model like Cloud to evaluate the agent’s understanding:
- Ask if instructions in the system prompt are ambiguous or make sense [10:47:00].
- Check if the agent knows how to use provided tool descriptions, and if it needs more or fewer parameters [10:54:00].
- Provide the agent’s entire trajectory to the model and ask why a certain decision was made, or how to help it make better decisions [11:02:00].
This helps bridge the gap between human understanding and how the agent perceives its world, leading to more effective environmental design [11:17:00].
Future Considerations for Agent Environments
Looking ahead, several future considerations will impact the design and optimization of agent environments:
- Budget Awareness: Agents need to become more budget-aware regarding cost and latency, which is essential for deploying them in production [11:47:00]. Defining and enforcing budgets (time, money, tokens) within the environment will be critical [12:02:00].
- Self-Evolving Tools: The concept of agents designing and improving their own tools and ergonomics will generalize, enabling agents to adapt tools for each use case, making them more general-purpose [12:21:00]. This implies environments that can facilitate tool creation and refinement.
- Multi-Agent Collaboration: The future will likely see more multi-agent collaborations in production, with agents having distinct roles and protecting each other’s context windows [12:42:00]. A key open question is how these agents will communicate with each other beyond rigid, synchronous user-assistant interactions, potentially moving towards asynchronous communication and enabling more diverse roles [12:59:00]. This will require new paradigms for designing shared or interconnected environments.
These areas represent challenges in AI agent development and require collective effort from AI engineers to address.