Engineering Perspectives

Engineering Agentic AI applications presents unique challenges and opportunities, requiring shifts in traditional software engineering practices. Drawing from business and product perspectives, here are some high-level and important themes:

Building Scalable and Resilient Agent Infrastructure

We need strong, reliable infrastructure that won’t fall over when lots of agents are working, especially when things get unpredictable. This means designing systems that can scale up, use resources wisely, and handle errors gracefully so they keep running.

Developing Data Pipelines for Agent Learning and Operations

A huge job is building the pipelines to feed agents the data they need to learn and make smart decisions. This involves handling tons of different kinds of data, making sure it’s good quality and secure, and often processing it instantly.

Designing for Human-Agent Collaboration and Interaction

Engineers need to design the system so humans can easily team up with AI agents. This means creating clear ways for people to see what agents are up to, give them feedback, or take the wheel if needed, making it a smooth partnership.

Implementing Continuous Integration and Deployment for Agents

Agents are always learning, so we need smooth processes (like CI/CD for AI) to update their ‘brains’ frequently without breaking things or interrupting service.

Integrating Trust, Transparency, and Explainability

It’s vital to build agents that people can trust. This means engineering ways for agents to explain why they did something, keeping good logs of their actions, and making sure we can check their work.

Ensuring Security and Privacy in Agentic Systems

Given the sensitive data agents may handle and their autonomous nature, engineering must prioritize advanced security and privacy measures. This includes securing agent communication, protecting data stores, implementing access controls, and mitigating risks associated with autonomous actions.

Engineering for Ethical AI Deployment

Agents might use sensitive data and act on their own, so security and privacy are critical. Engineers have to build in strong protections for data, control who/what accesses it, secure agent communications, and reduce risks from agents acting autonomously.

Facilitating Tool and Environment Integration

Engineering teams have the key role of turning ethical rules into actual working code. This includes finding and fixing bias in data or agent behavior, adding safety nets to prevent unintended consequences, and making sure the system follows ethical guidelines.