As someone who designs technology solutions to solve real-world enterprise challenges, I’m all too familiar with the hidden gap between an executive demonstration and deploying that solution to production. This is especially challenging when presenting AI-powered functionality that looks great on the surface, but fails to meet the strict requirements of subject matter experts.
Executives scrambling to leverage AI can increase the risk of teams falling for the latest AI hype, which we’re constantly pummeled with. It’s become challenging to find the signal in all the noise, resulting in wasted time and money. This is why I choose to refrain from introducing unnecessary complexity until it’s absolutely required to meet our goals. That approach has allowed our team to prove where AI works and where it doesn’t with minimal upfront investment.
With everything I’ve learned about building high-quality, scalable AI solutions I’ve finally found a product called Kong that cuts through the noise and aligns perfectly with the infrastructure I’ve been slowly building from scratch. Let’s give you a bit more perspective by digging into my experience working with AI.
My Approach to AI Solutions
The most common experience people have with AI is through chatbots. Your prompt might be as simple as asking a question to obtain some information. I’ve learned that prompting a model to get valuable information is a skill that many don’t have, especially if you’re asking a general AI model to perform a specialized task.
This is why enterprise AI solutions that I’ve designed eliminate the need for users to become prompt engineers by hiding an expert crafted prompt under-the-hood to be initiated with the simple click of a button. While this is slightly more difficult to implement technically, it optimizes the user’s experience causing them to almost forget that AI is doing work for them.
You can also kick off an entire AI workflow with a click of a button, which moves us into agentic solutions.
Challenges of Building AI Agents
Once you’ve moved beyond simple prompts, the complexity ramps up very quickly.
You need to carefully design how your agents connect to data sources to provide them with the appropriate domain specific context required to perform a specialized task. If you bring in too much data, your agents will hallucinate which could ruin an entire automated workflow.
You need to be able to track what each agent is doing, along with how many tokens it’s consuming, to ensure you don’t get caught in an endless loop that eats up your quota without any useful results.
Not to mention that you need to implement appropriate security to ensure agents aren’t performing actions that put your organization at risk.
Building tools to manage all this complexity from scratch is very time consuming and requires extensive planning and effort most enterprise teams don’t have.
That’s where Kong comes in!
Introducing Kong!
What I’ve been building manually, Kong has productized into a unified AI Connectivity Platform to optimize AI solution development and deployments. This means you can focus on connecting the tools your agents need to get the job done without having to build and manage the infrastructure to support them. You can even manage everything through their global dashboard called Konnect.
Kong AI Gateway
The Kong AI Gateway solves the production challenge I mentioned at the start of this article. It provides a single control plane where you can apply enterprise-grade security, PII sanitization, and prompt firewalls without changing a single line of your application code. This means you can move from a fragile demo into a hardened enterprise grade system with ease. Kong even manages the different request structures required to call different providers and models, which is something I’ve had to build out manually!
Kong Agent Gateway
The Kong Agent Gateway is a new capability within Kong AI Gateway that is designed to manage traffic communicating over the Agent-to-Agent (A2A) protocol, which is the protocol I personally chose to support my agentic solutions. This means you’re able to view real-time agent communication, LLM calls, and MCP usage through a single unified platform, which addresses major challenges related to debugging. Their full audit logging also helps with internal governance, which can be a critical detail in certain cases.
Kong Context Mesh
Kong’s Context Mesh helps you avoid flooding an agent with unnecessary data by acting as a semantic filter. It discovers your existing internal APIs and automatically transforms them into small, consumable chunks that agents can understand via the Model Context Protocol (MCP). It ensures an agent only gets the specific context it needs to produce accurate results. One of the features I personally love is that they offer a GUI, which is extremely valuable for enabling collaboration between technical and non-technical stakeholders.
Kong MCP Registry
The Kong MCP Registry acts like a “Yellow Pages” for your AI. Instead of hardcoding every tool into your agents, the agents can semantically discover the approved tools they need at runtime. This allows you to add new capabilities to your AI workforce instantly without a massive engineering overhead. This includes the ability to ensure only approved MCP tools are used, which helps your teams manage risk.
Summary
To be completely honest, I put up my usual “hype guardrails” when I first heard about Kong. I thought it was just an unnecessary middle layer that probably added cost and complexity. I eventually came to realize that they’ve already solved one of the biggest challenges I’ve been facing in my career. Especially considering that I’m on a very lean team where building out these valuable features means I wouldn’t be able to focus on enhancing our production pipelines with AI-driven automation.
I’m grateful to have run into Kong and highly recommend that you check them out. If nothing else, you have to checkout how AWESOME their website looks!
Tech-Multiverse is not affiliated with or otherwise sponsored by Kong, Inc.
