Risk: Agents may act on fabricated information, leading to flawed decisions and harmful outcomes.

Mitigation: Grounding responses with Retrieval-Augmented Generation (RAG), enforcing logical reasoning, and implementing self-consistency checks.

Risk: Attackers can embed malicious instructions in data to hijack the agent's control flow, causing unintended actions or data leaks.

Mitigation: Input sanitization, treating agents as privileged identities with least-privilege access, and monitoring for anomalous behavior.

Risk: Agents can inherit and amplify societal biases from their training data, leading to unfair or discriminatory outcomes.

Mitigation: Rigorous evaluation, trajectory analysis to detect biased reasoning, and maintaining a human-in-the-loop for sensitive decisions.

Risk: Multi-step workflows with many LLM calls can be slow and expensive, making them unsuitable for real-time applications.

Mitigation: Using smaller, specialized models (SLMs) for routine tasks and reserving large models for high-level planning and reasoning.

Risk: An error by one agent can cascade through a multi-agent system, leading to complete task failure that is difficult to debug.

Mitigation: Modular design with clear responsibilities, robust state management, and comprehensive observability to trace errors to their root cause.

Risk: The "black box" nature of LLMs makes it difficult to determine responsibility when an autonomous agent causes harm.

Mitigation: Ensuring transparency through agent observability, trajectory evaluation, and developing new legal/ethical frameworks for autonomous systems.