Is Your Business REALLY Ready to Deploy AI Agents in 2025?
Probably NOT - Here's why and what you need to succeed
Are you truly prepared for AI Agent development in 2025?
If you said ‘yes’, I don’t believe you! 😁
Companies are racing to develop AI Agents, blissfully unaware - like a bull in a china shop - that they’re completely unprepared.
From high-performance storage systems to robust security measures, today we'll dive into the critical components that ensure scalability, reliability, and efficiency of your Agent ambitions.
By the end, you'll understand the strategic (and tactical) steps necessary to prepare your enterprise for AI Agent development in 2025. There’s also a checklist at the bottom of this article you can take away and apply during your readiness analysis.
The Essential Architecture for AI Agents
To develop AI Agents, enterprises need a robust technology components akin to a well-stocked kitchen ready to serve a five-star meal.
High-performance storage systems are the pantry, supporting scalability and low-latency operations.
Vector databases act as the spice rack, essential for RAG implementations with their similarity search capabilities.
Compute infrastructure, including, are the kitchen appliances, powering the workloads.
From 60,000 feet, the landscape for success looks like this.
You’ll notice I’ve colour coded these components. The simple explanation for this is the criticality of each. You might have the best idea in the world for an AI Agent, but without the right Data Storage architecture or Messaging Sub-systems, you’re on a hiding to nothing. Why? Because your Agent won’t actually be able to do anything useful!
Without the right architecture, AI Agents are like chefs without a kitchen - full of potential but unable to deliver anything."
Let’s take a deeper dive into the subject by starting with the foundational components (in Red) and discuss the basics of what you need to get started building Agents properly in an enterprise environment.
#1 - The Foundation: Storage Infrastructure
To kick things off, let’s talk about storage infrastructure. High-performance storage systems are the backbone of any successful AI Agent development. These systems must support linear scalability, handle vast datasets, and offer low-latency read/write operations. Think of it as building a skyscraper; without a solid foundation, the entire structure is at risk.
In my own Agentic Framework (Templonix) I make significant use of:
Redis
Ideal for memory caching and retaining user context in real-time chat or human-in-the-loop interactions. Redis ensures low-latency responses for conversational agents.
MongoDB Atlas
Serves as both a document store and a vector store - In other words, this is the journaling system of what the Agent has done (allowing reflection) and also the the long term memory. Very few organisations will have any experience with vector stores until they start an AI journey.
SQL Databases
The workhorse for structured data management. Essential for maintaining enterprise-grade relational data integrity across applications.
Enterprises will also have parallel file systems and distributed storage to manage both structured and unstructured data. Making these available to an Agent is crucial for working with text, PDFs, and multimedia files as part of its goals and tasks.
#2 - Powering the Future: Compute Infrastructure
There’s a whole technology aspects I could tell you about this step, but I won’t - because the technology part isn’t really the point at this stage of the process; deciding on where you want to host your language model is.
Trade-offs Between Privacy and Cost
One of the most critical decisions enterprises face is whether to build their AI Agents on-premises with the LLM inside the walled garden, or leverage external API interfaces to large language models.
On-premises solutions offer complete control over data security and privacy, making them ideal for organisations with stringent regulatory requirements. However, this comes with a significant financial burden, as maintaining high-performance hardware and skilled personnel can be costly. Incidentally, I wrote a piece on the key costing considerations of AI Agent development here.
On the other hand, using APIs from providers like OpenAI and Anthropic can drastically reduce upfront infrastructure costs and accelerate development times. This approach, however, introduces dependencies on external vendors and raises concerns about data sovereignty and compliance. Balancing these trade-offs are pivotal for businesses aiming to integrate AI Agents effectively.
These are incredibly important design and business decisions that shouldn’t be made at the drop of a hat and will always involve WAY more people than you think with it comes agreeing the way forward.
#3 - Securing the Future: Rules, Roles & Responsibility
Security is super important in AI Agent development. While securing an AI Agent via role-based access and firewalls mirrors conventional applications, the real complexity lies in governance and guardrails in the context of this topic.
Governance
Preventing AI Agents from selecting tools or taking actions beyond their intended scope is critical. Without clear boundaries, you’re inviting chaos.
Guardrails
Implement technical mechanisms to constrain Agent behaviors. These include role-based access controls and predefined action hierarchies that are baked into the prompts that support the Agentic functionality.
In a previous article, Why Your AI Needs a Job Description, I write about these two points in detail. I’ve that this is pretty much the single biggest overlooked topic of AI Agent development - It’s still a huge blind spot, and has the potential to sink any Agentic project before it leaves the quayside.
The Glue: Working Inside the Walled Garden of Your Enterprise
Now that we’ve looked at the foundations of what’s needed, let’s now look at the glue that can elevate your Agent from a TV Dinner to a Michelin Star meal!
AI Agents need tools to be effective. The foundational components, like LLMs and vector stores, are just the beginning. In an enterprise context, integration with REST APIs, message queues, and file systems becomes critical.
Tooling and Plugins
Tooling and plugins provide the bridge between your AI Agents and external systems. Custom APIs or pre-built integrations allow Agents to access enterprise data, execute actions, or pull resources when needed.
Without this connectivity, the Agent's ability to function as part of a larger ecosystem is significantly limited, reducing its overall utility and value.
Common examples of such components are REST APIs surfaced as Plugins from systems such as a CRM or social media platforms like Twitter. They are referred to as Plugins as opposed to Tools because they use a polling pattern in order to interface with the core system because they are not fully event driven.
One of the best open source examples of the use and differences between the concept of a Tool and a Plugin is the Eliza framework from the AI16z team.
A Tool by contrast is available to an Agent both atomically and chained together. Below is an example of how my framework uses the tool chaining concept - If I ask the Agent to produce a Project Plan document from a template, it can work out which tools it needs to invoke to complete the request based on what I asked it to do.
Asynchronous Messaging
Asynchronous messaging systems like RabbitMQ or Kafka enable “proper“ communication between different components of your enterprise architecture. These systems are essential for handling long-running tasks, queuing actions, or managing workflows where human intervention might be needed.
I believe that asynchronous messaging sub-systems are the most underrated architecture components in Agentic development today. I wrote about this recently in the article Beyond the AI Hype: The Critical Infrastructure You're Overlooking.
Containers
In AI Agent development, frameworks like Agent Zero introduce the capability for Agents to autonomously generate and execute code at runtime, enhancing their adaptability and problem-solving skills.
This autonomy, while powerful, introduces significant security and stability concerns, as unrestricted code execution can lead to unintended system modifications or vulnerabilities.
Applying containerisation through technologies like Docker provides a robust solution by isolating the Agent's execution environment from the host system. This isolation ensures that any code executed by the Agent remains confined within a controlled setting, mitigating risks associated with autonomous code execution.
Monitoring
This part can be a bit complex in Agentic development and I view it from three angles.
Traditional tools like Prometheus and Grafana provide real-time metrics and alerting for infrastructure components, enabling detection of anomalies such as increased latency or resource exhaustion. Nothing new here. This foundational monitoring is crucial for maintaining the health of the underlying systems that support AI Agents.
Beyond infrastructure, specialised tools like LangSmith offer in-depth tracing and debugging of AI Agent behaviors. LangSmith captures detailed execution traces, including API calls and decision points, facilitating the identification of issues such as infinite loops within an Agent's Reflection pattern. By analysing these traces, developers can pinpoint the exact step where the Agent's task refinement process becomes stuck, enabling targeted interventions to resolve the problem.
The third one is novel to the Agentic space. Terminal solutions like Luna's Brain focus on verifying and validating the 'personality' and decision-making processes of AI Agents. They ensure that Agents adhere to predefined behaviors and ethical guidelines, providing an extra layer of oversight. In the context of an Agent caught in an infinite loop during task refinement, such tools can help assess whether the Agent's actions align with its intended persona and operational parameters, ensuring consistent and reliable performance.
Implications & Outcomes
I hope you can see that paying attention to, and investing in the right architecture for AI Agent development offers numerous benefits. It ensures scalability, reliability, and performance, allowing your enterprise to integrate AI seamlessly into their operations.
Strategically, such an investment opens up new possibilities for innovation and growth. As AI technology continues to evolve, having the right “Agent compatible” architecture will be a competitive advantage.
Key Takeaways
Rather than just listing points, let me break down what I think are the crucial insights:
🔧 Build a Solid Foundation
Investing in high-performance storage systems like Redis, MongoDB Atlas, and SQL databases ensures your AI Agents have the backbone needed to handle vast datasets and deliver low-latency responses. Without these foundational systems, achieving scalability and reliability will be impossible.
🚀 Power Your AI with the Right Compute According to Your LLM Type
Carefully balance the trade-offs between privacy, cost, and deployment speed to find the best solution for your enterprise. This can be a costly mistake if you get it wrong.
🌉 BUILD Custom Tools and Plugins for the Best Integration Experience
Creating bespoke tools and plugins is essential for AI Agents to effectively interface with enterprise systems. This capability enables agents to access and interact with existing business applications, databases, and services, ensuring cohesive operation within the organisational infrastructure.
🛠️ Leverage Development Tools
Robust use of containers, monitoring systems, and asynchronous messaging platforms are essential for building, deploying, and debugging AI Agents. These tools not only streamline development but also ensure long-term operational reliability and adaptability - not to mentioned, reduce the prospect of your Agent running del *.*!
The AI Agent Readiness Checklist
Everything discussed today has been added to this handy PDF checklist. You can grab your copy below.
If you're using Agents or planning to do so soon, I'd love to hear about what you’re doing and any challenges you’re running into.
Drop a comment below or reach out directly - I read every response.
Until the next one.
Chris