Agentic AI seems to be talked about constantly and as I’ve talked about in previous articles design patterns matter – same thing applies to AI Agents, design matters. What design patterns, or a least design considerations are there for Agent AI design?
What is it?
Agentic AI refers to AI systems capable of autonomously performing tasks, making decisions, and adapting to new situations to achieve specific objectives. These systems have a degree of “agency,” allowing them to operate with minimal human intervention. The design of Agentic AI systems is crucial as it determines their effectiveness, reliability, and safety in real-world applications
Key design considerations include:
- Balancing non-deterministic and deterministic behaviour
- Optimising the number of tools assigned to each agent
- Deciding between single-agent and multi-agent architectures
These factors significantly impact the system’s accuracy, performance, scalability, and ability to handle complex tasks.
What does it mean from a business perspective?
Implementing Agentic AI requires careful design considerations to maximise benefits while minimising risks and realising efficiency increases:
- Consider Accuracy and Task Allocation: Deciding between assigning tasks to non-deterministic agents or deterministic tools is critical. Non-deterministic agents are suitable for complex, adaptive tasks, while deterministic tools are preferable for straightforward, rule-based tasks.
- Address Scalability and Complexity Challenges: As tasks become more complex, determining when to transition to a multi-agent system is crucial for maintaining modularity, efficiency, accuracy and assisting in trouble-shooting. This transition also allows the system to be more easily adapted to changing business requirements.
- Security Concerns: The autonomy of Agentic AI introduces new attack vectors and risks, necessitating robust security measures.
- Compliance and Oversight: Balancing agent autonomy with necessary human oversight is essential, especially in sensitive industries and processes.
What do I do with it?
To leverage Agentic AI effectively in your organisation your Solution Architects, Business Analysts, AI Operations teams, Security and Testing teams need to be involved in multiple aspects of the Agentic system design:
- Start Small and Develop Design Principles: Begin with basic, repetitive tasks that can be easily automated. This allows you to gain experience and build confidence in the technology. At the same time develop design principles that work for your use-case (e.g. The split between Agent and Tool workload for non-deterministic vs deterministic requirements; Agent workload – when to lessen workload on a single Agent, i.e. complexity and tool ratio, and move to a multi-agent system.)
- Design for Modularity: Develop agents with specific roles and capabilities to enhance system modularity, making it easier to update and scale individual components as needed.
- Implement Safeguards: Develop clear guidelines for when to use deterministic tools versus non-deterministic agents and when a ‘human in the loop’ is required. For critical or sensitive tasks, lean towards deterministic approaches, for creative tasks a more non-deterministic approach may be better.
- Test, Monitor and Iterate: Test your Agent so you thoroughly and understand it’s potential. Continuously evaluate your Agentic AI’s performance. Be prepared to adjust the number of tools assigned to each agent or transition to a multi-agent system if complexity increases
- Prioritise Security: Implement robust security measures, including API protection and non-human identity management and authentication, to mitigate risks associated with Agentic AI
- Foster Collaboration: Encourage cooperation between your AI and human teams. This hybrid approach helps ensure proper oversight
By carefully considering these design aspects, you can harness the full potential of Agentic AI while maintaining control and minimising risks. Remember, the key to success lies in striking the right balance between autonomy and determinism, tailored to your specific business and process needs.
Further Reading
AI Engineering Lessons from Pulumi
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