Additionally, the article explores the varying definitions of what constitutes an "agent" in the AI space, noting that there is no consensus on a single definition. Some contributors suggest that an LLM becomes an agent when it can make tool calls and perform actions based on those calls, while others argue that the term is too broad and lacks specificity. The conversation also addresses the reluctance of companies to publicly acknowledge their use of LLMs due to competitive and reputational concerns. Overall, the article presents a critical view of the current state of agentic AI, emphasizing the gap between the hype and the reality of what these systems can achieve.
Key takeaways:
- The concept of "Agentic AI" is often seen as overhyped and misunderstood, with many implementations being simple workflows involving prompts and context management rather than true autonomous agents.
- There is a lack of consensus on the definition of "agents" in the AI space, with various interpretations ranging from LLMs making tool calls to more complex autonomous systems.
- Many companies are using language model-based systems but are hesitant to publicize their use due to competitive concerns and potential public backlash.
- Agentic workflows are often criticized for being more suitable for demonstrations rather than practical business applications, as they can suffer from issues like hallucination and lack of reliability.