Building the Agentic Platform: A Guide for Platform Engineering Teams
The rise of Large Language Models (llms) is rapidly shifting the landscape of software development, ushering in an era of AI Agents. These aren’t just chatbots; they’re autonomous entities capable of complex tasks, data analysis, and workflow automation. But unlocking the true potential of agents requires more than just powerful models. It demands a robust, well-engineered platform – and that responsibility increasingly falls to platform engineering teams.
This article dives deep into the critical considerations for building an agentic platform, outlining the challenges, opportunities, and best practices for creating a secure, scalable, and governable environment for your organization’s AI-powered future.
The Agentic Shift: Why platform engineering Matters now
For years, developers have focused on building applications. Now, they’re building agents - and the underlying infrastructure needs to adapt. Just as platform engineering emerged to streamline the development and deployment of traditional applications, it’s now crucial for managing the complexities of agent-based systems.
The core principle is simple: abstract away the complexities of agent infrastructure so developers can focus on what the agent should do, not how it does it. This means providing a standardized, reusable foundation for common agentic requirements. As Marco Palladino, CTO of Kong, puts it, “Ther are lots of crosscutting requirements that every agent needs to have. The platform teams-now the ball is in their court.Come up with a platform that can help all of these developers build agents that are, by default, secure, observable, governable, and so on.”
key Components of a Robust Agentic Platform
Building this platform isn’t a trivial undertaking. Here’s a breakdown of the essential components:
* Routing & orchestration: Agents frequently enough require access to multiple tools and services. A sophisticated routing infrastructure is vital to direct tool calls to the correct endpoint with the appropriate authentication and structured data format. This includes the potential need for Model Call Procedure (MCP) servers to manage complex interactions.
* Observability & Monitoring: agents are, by their nature, more dynamic and less predictable than traditional applications. Thorough metrics, logging, and tracing are essential for understanding agent behaviour, identifying bottlenecks, and ensuring reliability. This also extends to evaluating the agents themselves – leveraging LLMs to assess performance and identify areas for improvement (as highlighted in recent research on LLM-on-LLM evaluations).
* Security & Governance: Agents frequently handle sensitive data and interact with critical systems. Robust security measures are paramount,including:
* Prompt Engineering & Guardrails: Implementing controls to prevent prompt injection attacks and ensure agents adhere to defined boundaries.
* Response Filtering: Sanitizing agent outputs to remove potentially harmful or inappropriate content.
* Data Access Control: Strictly limiting agent access to only the data they need, based on the principle of least privilege.
* Authentication & Authorization: Securely managing access to tools and APIs.
* Data Connectivity & Management: many agentic applications revolve around data processing. Your platform must provide secure and efficient connections to various data sources, including databases, data warehouses (like Snowflake), and APIs. This includes capabilities for data cleaning, change, and presentation. jeff hollan, Director of Product at Snowflake, emphasizes the potential for agents to dramatically accelerate data workflows: “How do I connect the right data? How do I clean the data? How do I get the data presentable? All of those tasks that data scientists, and data engineers, and data analysts are doing, can we help them do in an hour what maybe would’ve taken them a day?”
* Cost Optimization: Running multiple models for cost efficiency and evaluation adds complexity. The platform should facilitate model selection, resource allocation, and cost tracking.
* Tool Registry & discovery: As organizations scale, it’s easy to lose track of available resources. A centralized registry of tools, applications, and APIs – including seat availability and access permissions – can significantly boost agent development and encourage reuse.
Build vs. Buy: Navigating the Ecosystem
You have options when it comes to building your agentic platform.
* Leveraging Existing AI Provider Capabilities: Major AI providers (like OpenAI, Google, and Anthropic) are increasingly integrating agentic workflows into their products, often through plugins. This can be a quick way to get started, but it may come with vendor lock-in and limited customization.
* Building a Custom Platform: If you have a thriving ecosystem of