AI Agent Development Life Cycle with Sierra

The AI Engineer Summit wrapped up a few weeks ago in New York. If you missed it, don’t worry- I’m here to share key insights on AI agents!

Zack Reneau-Wedeen from Sierra highlighted a new framework- Agent Development Life Cycle- that is helpful for both enterprise leaders and AI engineers.

Here are some helpful ideas from Zack:

Every AI agent is a product

AI engineering and product management should be part of the development team. In other words, the development and application teams should not be siloed. Rather, they should work tightly to maximise the effectiveness and quality of AI agents.

This is even more prevalent in AI agents compared to traditional software engineering. Traditional software engineering is deterministic, while AI agents are stochastic. As such, the development team has to work closely with the application team to ensure that the AI agent is performing well. It cannot be a 'do once and forget' arrangement, as AI models and data will drift over time.

AI Development Life Cycle (ADLC)

Zack proposes an AI Development Life Cycle framework to help developers work closely with customers in production to deliver the best results.

Here are the steps:

1. Development: Define goals and guardrails of agent.
2. Testing: Conduct regression tests based on quality assurance of historical conversation.
3. Release: Manage releases with immutable agent snapshots.
4. Quality assurance: Audit and evaluate real world applications.
5. Alignment: On agent behavior with business and tech teams.

What stands out in ADLC is how closely the development and application team has to work together- the technology should improve the quality of the application, while feedback from the application will further improve the technology.

If you are an enterprise leader, it is important to remember that AI application should be clearly defined with KPIs. The ADLC helps to reinforce the notion that by close alignment of development with users and mutual feedback will lead to best results.

Do you have similar stories of how your tech team worked closely with its users to see clear ROI? Leave a comment below. 👇