Platform engineering has traditionally focused on building curated infrastructures and standardized tools to support operations and, above all, unburden developers from age-old complexity, allowing them to achieve greater productivity, foster more creativity and deliver more valuable products.
Now, significant advancements in agentic AI are reshaping the whole platform engineering paradigm, awakening developer latent innovation capacity but also raising some concerns worthy of consideration.
Specifically, integrating advanced AI capabilities into existing internal developer platforms (IDPs) is complex and requires solid governance frameworks to manage AI security and transparency effectively.
The key is establishing standardized guidelines, policies and best practices that are suited to diverse AI use cases, integrating them by design to harness agentic AI while keeping it safe and compliant.
Since these new guardrails primarily address agentic AI in the current platform landscape, are we on the verge of a change where platform engineering becomes AI agent engineering? Or is it simply growing to enclose this new domain?
The Rise of Agentic AI
Generative AI (GenAI) won organizations over little by little, but it left many with considerable disillusionment and the feeling they were always only scratching the surface, envisioning little to no benefit in the long run.
Using chatbots and assistants to help developers automate their workflows is one way to increase efficiency, but simply deploying smarter models to iterate platform automation is only a small part of the equation.
It’s like having a professional mirrorless camera only to keep it set constantly on automatic mode, missing details that could boost performance even more.
That’s why several organizations are acknowledging the need for more intelligent ecosystems with modular, autonomous agents embedded into workflows, shaping a living agentic mesh that drives conscious decision-making. But this doesn’t come without challenge or risk.
What Is AI Agent Engineering?
Platform engineering is evolving. It’s evolving so rapidly that it’s unclear if it’s just expanding to incorporate AI processes or is becoming something else.
AI agent engineering is about embedding context-aware systems into the foundation of an IDP, powering a specialized infrastructure. These systems can sense, interact, plan, reason and collaborate, helping developers hyper-automate workflows, solve intricate problems and build smarter applications.
However, such a degree of autonomy demands a solid AI governance framework, defining constraints and continuous oversight to scale AI agentic systems safely and confidently within enterprise platforms.
AI Governance Means More Than Just Taming AI Agents
AI agent engineering tries to make platform engineering smarter with automation and non-deterministic processes, yet it adds a layer of complexity concerning the governance of a sprawling deployment of disruptive AI agents.
AI governance is a much broader discourse than just setting guardrails to control AI agent behavior. AI governance involves key areas, such as: Establishing authorization for accessing specific network policies; Ensuring compliance with data sources, security and privacy policies; Planning strategic initiatives or specific use cases that align with overall business goals.
In essence, it’s a cultural challenge that addresses both technical and ethical aspects.
AI Agent Engineering: Why AI Governance Matters
The urgency of reliable and solid AI governance models is growing exponentially. This escalation follows the latest international regulations that are pushing for a sustainable AI adoption, granting it is safe, transparent and respectful of human rights and existing legal frameworks.
Legally binding examples include the recent EU AI Act or the Framework Convention on AI, but there are also notable frameworks and standards that offer globally recognized guidance and reference, such as the ISO/IEC 42001 (the world’s first AI management system standard) or the NIST AI Risk Management Framework.
Gartner corroborates the rush for structured AI governance in their recent research:
- By 2027, 75% of AI native platforms will integrate AI governance and responsible AI capabilities, making them key areas of AI competition.
- By 2027, all global AI laws and regulations will mandate AI governance.
- By 2028, uncontrolled AI agents, pursuing misaligned goals or ignoring constraints, will be the primary concern for 40% of Fulfill organizations.
- By 2030, fragmented AI regulation will be 4 times as great, covering 75% of global economies and costing $1 billion in compliance. At the same time, formalized governance frameworks could significantly reduce compliance expenses, freeing up budgets for strategic growth investments.
Creating Guidelines From The Beginning
The basic premise of AI agent engineering is setting a solid foundation to sustain the safe and confident scaling of AI agents within IDPs, creating guidelines by design.
However, the goal isn’t to make the IDP a gatekeeper for AI. The goal is to make the IDP the easiest, safest, most secure, most compliant and most cost-effective way for developers to build enterprise production ready solutions, cooperating with conscious agents.
Embedding guidelines into the IDP from the beginning means turning AI governance from a compliance obligation into an active and fundamental part of the development life cycle that gives strategic advantage, balancing fast workflows with the confidence of control.
It’s a structured approach that turns broad AI policies into clear operational controls, such as standardized documentation and lists of assets. These controls are then integrated directly into the system design via policy engines and architectural blueprints. Automated monitoring and active feedback loops based on real use cases help refine the guidelines, ensuring the proactive enforcement of AI governance.
Composable Platform To Harness AI Agent Engineering
As platform engineering is evolving, so is doing the IDP. AI agent engineering must cope with emerging challenges such as regulation pressure and legacy systems dependencies that are slowing down the scaling of agentic AI.
A composable platform could be your single instrument to standardize human-AI cooperation with templatized assets and best practices: A seamless experience that allows both developers and AI agents to operate contextually, abiding by the same rules.
With composable platforms, developers can craft new applications faster and more securely, unlocking the full potential of AI agent engineering:
- Embedded policies and guardrails are AI agents and developers’ safety belts, while self-service access to tools and services guarantees sustained throttle.
- Data solutions for integration and governance ensure AI agents consume data that is always spot on, reliable, traceable and updated.
- Catalogs are the gateway to discover, compose and reuse every bit of information within the organization, driving agility and responsiveness.
Takeaways
The rise of AI agents is gradually changing platform engineering, unlocking new possibilities that were once unimaginable.
However, agentic AI can be unpredictable when unrestrained, demanding strict governance that is non-negotiable. Therefore, now that most guardrails are set on agents, it is worth wondering if platform engineering is leaving room for AI agent engineering.
AI agent engineering is not a totally new discipline, but rather an augmentation of platform engineering that makes sure human developers and AI agents can live symbiotically within the same ecosystem to boost productivity while gaining control over IT assets.
Mia-Platform, an AI-native composable platform, can be one solution to harness AI agent engineering, accelerating time to value while adhering to stringent regulations.
