Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

AI agent hallucinations and excessive token costs stem from inefficient context management and reliance on probabilistic prompt instructions. Implementing semantic tool selection significantly reduces token waste by filtering relevant tools per query. Transitioning from standard RAG to Graph RAG enables precise, verifiable data aggregation by leveraging structured knowledge graphs rather than text sampling. To ensure reliability, multi-agent validation frameworks—consisting of executor, validator, and critic roles—effectively catch failures before they reach the user. Furthermore, enforcing constraints through neurosymbolic guardians and runtime steering ensures logic is executed as code, preventing models from ignoring rules. These techniques, when integrated into production environments like Amazon Bedrock Agent Core, transform agents from unpredictable conversationalists into robust, verifiable systems capable of self-correction and accurate task completion.