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Getting started with AI agents (part 2): Autonomy, safeguards and pitfalls
- Applying safeguards to AI agents is critical in reducing errors, waste, legal exposure, or harm when agents operate autonomously.
- Using determinism and defined rules to trigger human intervention decreases the risk of noncompliant behavior while keeping autonomous agents operational.
- Agents can be paired up with a checking agent that verifies for unethical or risky behavior, giving a go-ahead to proceed or not.
- Multi-agent systems require different testing, monitoring, sandboxing, and fine-tuning regimes to operate safely within large organizations.
- Inconsistencies in LLM-based agents are compensated for using fine-tuning and generative AI techniques while protecting against overloading agents with detailed instructions.
- Agents benefit from being divided into multiple connected agents to mitigate problems stemming from tailspins, where agents perpetually communicate, and ambiguous definitions of roles and purposes.
- Defining workflows as pipelines can reduce complexity by eliminating the coordinator agent, creating better-defined roles, and reducing the chance of a single point of failure.
- Multi-agent systems should be built with the intention of including generic placeholder tools to bootstrap and operationalize agents in an agile manner.
- Context management in multilevel communication chains needs to be managed to reduce transport overload and confusion over target purposes.
- There is a high bar for LLMs used as agent brains, which necessitates cost and speed considerations when creating and implementing multi-agent systems at scale.
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