Why we built ADK 2.0
Moving AI Agents from prototype to production creates new challenges. In real-world enterprise environments, agents can get stuck in infinite loops, bypass key business logic due to hallucinations, or fail without raising clean exceptions. Methods focused on the model, like guardrails, skills, and prompting, can only go so far.
- ▪Moving AI Agents from prototype to production creates new challenges.
- ▪In real-world enterprise environments, agents can get stuck in infinite loops, bypass key business logic due to hallucinations, or fail without raising clean exceptions.
- ▪Methods focused on the model, like guardrails, skills, and prompting, can only go so far.
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Moving AI Agents from prototype to production creates new challenges. In real-world enterprise environments, agents can get stuck in infinite loops, bypass key business logic due to hallucinations, or fail without raising clean exceptions. Methods focused on the model, like guardrails, skills, and prompting, can only go so far. For production-grade reliability, you need full deterministic control over your application flow.The core issue is structural. Large language models are frequently tasked with execution orchestration—handling tasks like routing, scheduling, and error handling that traditional code already excels at.
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