A 5-point checklist before you select and implement an AI agent platform

As a CIO, it’s likely that this year, your IT team is moving quickly from evaluating and deploying discrete third-party AI software packages, to integrating custom AI agents throughout your client-facing and internal business applications for even further automation and productivity. 

They’re likely working with at least one AI agent builder platform and perhaps several others. Since all AI tools, including AI agent builder platforms, are continually being innovated and updated, often on a daily basis, it’s worth keeping in mind a few things when selecting these tools and settling on one that’ll become your go-to platform. In light of this, here are some finer points to consider that go beyond the basics of functionality and pricing, yet can still be major determinants of success.

Assess the agent building environment

If we start with the agent building environment itself, the agent vendor is often learning and innovating as they go. They’re often well versed in LLM providers and models, and understand the pros and cons of one model over another. Yet they may have fewer skills on UI and taking a customer-centric view of their builder environment. Their entire studio may change frequently so you may create agents and migrate to newer versions of their studio simultaneously.

Ensure their environment is intuitive, easy to test your agents within, and has enhanced options for your agents such as short- and long-term memory. Plus, there should be features for responsible AI — reflection, groundedness, and context relevance — and safe AI — fairness and bias, toxicity check, human-in-the-loop, and PII redaction. You’ll also want to have, at a glance, visibility into your credits used as part of your subscription, as well as value-added features like the ability to improve the role and instructions for your agent using AI.

Thorough API documentation

Once your agents are built in the AI agent builder platform, the next step is to use API calls to implement these agents within your own applications. Look for plentiful documentation at the API level, but also higher-level information that explains the sequence when provisioning agents on the fly, and so on. This is where clear documentation can help your own IT team get up to speed and learn the required sequence from environment setup, to RAG creation and training, to agent creation, to agent interaction and inquiry. 

They’ll also need clear documentation on how to monitor and report on token usage, and how to monitor and display historical inquiries, AI agent and security performance, and integration with other systems. Having this information can often halve your development and testing time since there’s far less back-and-forth between your IT team and the agent provider resolving questions and issues.

Access to professional services and support

With flux in the agent builder environment due to continuous innovation, it’s important your vendor has professional services and support so they can assist your team in their implementation journey, which helps instill confidence in their technology and build trust.

Look for vendors that are generous in their support and willing to collaborate and partner with your team to get your agents to the finish line. If you can gain access to the leadership team, that’s even better, and they can give strategic advice on how to best utilize their platform and take advantage of various programs such as AWS partnerships and their own affiliate programs.

Ensure system uptime

System uptime of the AI agent infrastructure is often something that can be monitored via API calls and is critical to successful deployments. Since many of the AI agent builder platforms are startups, you may find the environment may occasionally experience some downtime as they migrate to newer versions of their platform, or make other changes to their APIs and their agent, RAG, and tooling environment.

Look for built-in ways to monitor this such as via API calls or ways you can easily reach out to someone at the company for troubleshooting. In terms of AI agent performance monitoring, you’ll want to look at response time and accuracy, and agent availability. In terms of error rates, check for the frequency and types of errors encountered by the agent such as incorrect responses, hallucinations, and mistakes retrieving information. In some cases, the agents just lose their context so it’s important to look at settings such as short- and long-term memory, and find out exactly how many inferences the agent retains in its contextual memory.

Explore the product roadmap

Once you’ve cleared the previous considerations in terms of building and deploying production-grade agents, you’ll also want to explore the vendor’s product roadmap. For example, if their agents provide textual output, do they have a plan to move to multi-modal with audio, images, and video? Is this something you’ll likely need in your own deployments?

Support for third-party tools and integrations will also be critical. For example, if vendor agents provide integration with X for social media posts, do they have planned support for LinkedIn as well? Do they have ready examples as they roll out new features?

The good news is that AI agent builder platforms are getting better every day with more intuitive interfaces, richer documentation, more integrations, and more use cases. The pace of innovation is staggering with founders often starting their day long before dawn just to keep up. If you’re partnered with a robust AI agent builder platform, you can free your IT team to focus less on the AI plumbing and more on the business rules, user interfaces, and integrations that’ll make your agent implementations successful.