Artificial Intelligence (AI) agents are one of the hottest topics in the AI space right now. Companies are racing to build autonomous systems that can handle complex tasks, but not everything labeled as an “agent” truly fits the definition. The team at Anthropic recently shared their insights on what makes an effective AI agent, distinguishing between workflows and true agentic behavior. Let’s dive into what they had to say and how you can apply these principles to building your own AI agents.
What is an AI Agent?
One of the most common misconceptions is that any system using an AI model qualifies as an agent. However, as Erik from Anthropic explains, there’s a distinction between workflows and agents:
- Workflows: These involve a predefined set of AI calls and processes. For example, taking user input, processing it through a sequence of prompts, and returning a final output. The system follows a rigid, structured approach.
- Agents: Unlike workflows, agents exhibit autonomy. They decide how many steps to take, what tools to use, and when to stop. A true agent continues to loop through actions until it finds a resolution.
A simple example of a workflow would be classifying a user query and routing it to a predefined response. In contrast, an AI agent would engage in an open-ended process, refining answers, running additional checks, and iterating until it reaches the best possible outcome.
Key Patterns in AI Agent Development
1. Providing the Right Level of Autonomy
AI agents need to operate autonomously but within controlled limits. They should be capable of executing multiple steps dynamically, yet developers must ensure the agent stays within useful bounds. For example, a customer support agent should iterate through problem-solving steps, but it shouldn’t loop indefinitely.
2. Well-Designed Tooling & Documentation
A surprising challenge many AI developers face is poor tool descriptions. According to Erik, many developers put great effort into prompt engineering but provide tools with unclear names and zero documentation. AI models, like humans, need clear and descriptive tool instructions to perform efficiently.
3. Empathizing with the Model’s Perspective
Barry shared an interesting anecdote about debugging AI behavior. He and a colleague would close their eyes for a minute, then briefly look at the screen before shutting their eyes again, mimicking how an AI might process information in bursts. This approach helped them understand why certain agent behaviors seemed counterintuitive. The lesson? Think like the AI when designing prompts and decision-making pathways.
What’s Overhyped and What’s Underhyped About AI Agents?
- Overhyped: Consumer-facing agents (like vacation booking bots). The challenge is that defining exact user preferences is often as difficult as performing the task itself. Moreover, consumers don’t want fully autonomous actions like booking a flight without human approval.
- Underhyped: Small, time-saving automations. Even shaving off a minute per task can have massive productivity impacts at scale. AI agents that assist in repetitive coding, document summarization, and iterative search are prime examples.
The Future of AI Agents in 2025
Anthropic’s team predicts that AI agents will see widespread business adoption in the near future. However, while single-agent systems are still being refined, multi-agent environments—where multiple AI agents collaborate—could be the next frontier. Barry even experimented with AI agents playing social deduction games like Werewolf to explore how they interact in complex scenarios.
Practical Tips for Developers
- Measure Results: Before scaling up an AI agent, ensure you have metrics in place to evaluate its effectiveness.
- Start Simple: Many successful startups optimize their workflow with a single LLM call before moving to complex agentic behavior.
- Build for Future Model Improvements: Your AI system should become more effective as models improve, rather than becoming obsolete.
Conclusion
AI agents are evolving rapidly, but their implementation must be practical and results-driven. Many companies rush to apply agents to problems where simple automation would suffice. Instead, focus on real-world use cases that benefit from iterative, autonomous decision-making. By designing thoughtful workflows, providing clear documentation, and measuring effectiveness, developers can build AI agents that truly enhance productivity.
If you’re developing AI-driven solutions, now is the time to experiment with agentic frameworks while keeping scalability, reliability, and practical utility in mind.
