Implementation challenges and solutions

- Let's take a look now at the challenges of implementing agents in the real world and explore some practical solutions. The first challenge we've all heard about with AI is hallucinations and general reliability. This can lead to incorrect actions being taken and they can compound over time. The solution is to implement verification loops where agents should validate their reasoning before they take action. Now, this can include first, having agents cite their sources for fact checking. Then, you can have a process to verify these sources, including training data to check for accuracy. You can use RAG, Retrieval Augmented Generation, to ground your responses in verified data by having a knowledge base lookup, and of course, you can implement human in the loop approval for high stakes decisions. The next big challenge is complex orchestration. - [Crew] Sorry, I'm going to interrupt you there. - As your agent ecosystem grows, orchestrating multiple agents becomes increasingly difficult. One solution is to adopt a modular architecture where you have clear role definitions for each agent. For example, in this diagram, I have separate agents for data retrieval, content generation, fact checking, and output formatting. You do need standardized communication protocols between agents. A central orchestration layer to manage the interactions, as well as logging and observability tools to track the flow of information. Now, while this all looks more complex, the separation of concerns makes your maintenance easier and lowers TCO. Another challenge, of course, is security and safety. If agents have access to systems and data that pose security and safety risks, if not properly contained, then you should implement strict permission boundaries with sandbox environments, you should create tiered access levels based on the agent's responsibilities, and of course, you should establish guardrails to prevent harmful outputs or actions, all while developing automatic monitoring systems to detect any unusual behavior. The next challenge, of course, is cost management. Unoptimized agentic systems can generate many excessive API calls, and calls can be typically charged per volume, leading to spiraling high costs. But smart design can mitigate this. For example, you can implement caching strategies for frequently requested information, so you don't need to keep calling the same expensive backend model for the same stuff again and again and again. Simpler tasks can also use alternative, smaller or even free models. Well-designed efficient prompts and prompt templates can minimize your token usage. And of course, rate limits and budget thresholds with automated alerts could be implemented. Now, these are just a few ideas. Of course, your system may have other opportunities to be efficient without sacrificing functionality. Another challenge is maintenance and evolution. Deploying an agent to production with an ops mindset means you have to think about more than just the agent. Indeed, the agent itself, as you can see, is just a small part of your overall system, and that's just for one agent. Running one constantly and keeping it running well is a process of ongoing operations and maintenance. Ongoing user feedback and benchmark tasks also need to be evaluated against your desired metrics. Requirements and documentation updates can impact the agent directly, and the test results of doing this will also need to be evaluated. From all of that evaluation and system updates, you then feed back to documentation and requirement updates, which will help you in the planning of building a new version of the agent. And a Continuous Integration/Continuous Deployment or CI/CD can then deploy a new version of the agent code to production and the process begins again. Implementing agentic frameworks requires you to address these key challenges head on. By focusing on reliability, orchestration, security, cost, and maintenance, you can build robust agentic systems that will deliver real value while also minimizing risks. In the next video, we're going to go into a little bit more detail and look at the core components of agents and what makes them tick.