The race to accelerate modernization across business units within an organization has led to an API explosion, but with little consideration for how AI is changing the way different software systems communicate with each other, share data and trigger actions. 

We’re seeing AI tools not only consume APIs at an unprecedented rate but also accelerate their creation, which in turn spurs uncontrolled proliferation and decentralized management that surfaces new challenges and risks, especially if the underlying API ecosystem isn’t well managed. The nature of how AI interacts with systems requires a new kind of API paradigm – one that’s more dynamic, more context-aware and often more complex. 

How we build, manage and secure connections between different services and systems is undergoing a profound shift. “AI is reinventing the way we connect resources and data in our organizations. Integration is no longer about binary interactions with fixed logic,” says Matt Voget,  VP of engineering at Ambassador, an API development platform that offers tools to improve developer experiences. He explains, “The ability to interface fluidly with data and services in a non-linear way that is informed by context is the magic of AI. To enable that, the connective tissue between resources – APIs and other emerging technologies like MCP servers – must be available and adaptable.” And so, for businesses, this means re-evaluating whether your current API infrastructure is flexible and discoverable enough to support these more intelligent, context-driven AI interactions. 

According to Rajesh Kumar Pandey, principal engineer at Amazon Web Services (AWS), “it’s not subtle. APIs are becoming the glue for every AI-powered interaction model, calls, data retrieval, chaining steps, everything.” He adds, “and now, with GenAI tools spinning up APIs on demand, we are seeing explosive growth. What’s changed is not just the volume but the volatility. APIs are being created faster than they’re documented, governed or even acknowledged.” This rapid, often undocumented, growth requires organizations to implement systems to track and manage APIs with a new sense of urgency before this volatility leads to chaos. 

Moreover, this isn’t just about external-facing APIs either. Ankit Awasthi, director of engineering at Twilio, observes that the change is particularly noticeable “with the rise of AI agents that use APIs as tools to reason, plan and act.” He explains, “This agentic pattern massively increases API invocation frequency and surfaces even more APIs, especially internal ones that were never designed for autonomous consumption.” Therefore, a critical consideration for any AI strategy must be how to prepare and govern these internal APIs for safe and effective use by autonomous AI agents. 

What Could Go Wrong With AI-Driven API Development? 

The speed at which AI, particularly generative (Gen) AI, allows developers to create and iterate is highly advantageous for faster innovation and initiating more ambitious projects than ever before. 

But this introduces a set of emerging risks that can turn AIʼs power into a double-edged sword if not managed with foresight. When code, including critical API interfaces, is generated rapidly without the traditional cycles of deep architectural review or meticulous security planning, organizations can inadvertently introduce new vulnerabilities, quality issues and long-term manageability headaches. 

One of the most talked-about aspects is this rapid, AI-assisted code generation, sometimes dubbed ‘vibe coding’. Voget acknowledges that while AI empowers developers, vibe coding opens new doors for developers looking to expedite their work. However, he cautions, “The danger is in the ability to generate functioning code without the skill or experience to catch dangerous vulnerabilities or fatal flaws. APIs are no different than other code – vibe coding can be rife with unintended consequences.” This means that while embracing AI tools, it’s imperative to reinforce rigorous code review and security testing, ensuring that human expertise validates AI-generated outputs. 

Pandey calls this ‘vibe-driven development’, pointing out that “it is fast, it demos well, but under the hood? It’s often messy.” He reasons, “You end up with APIs that skip validation, ignore rate limits and have zero observability baked in. It’s not that GenAI is bad at coding, it is that it lacks system-level context.” That’s part of why organizations must invest in processes and tools that can inject this missing system-level context and enforce non-functional requirements even for AI-generated APIs. 

Arnold Pinkhasov, software engineer at Chronos, paints a stark picture of what happens when this goes unchecked, stressing that vibe coding introduces ‘massive potential chaos into a codebase’. If GenAI tools produce “superficially functional API endpoints, that doesn’t mean they’re safe, scalable or aligned with the system’s conventions.” They often lack “idempotency, observability hooks or coherent versioning, making them fragile and unmaintainable.” His warning that “what’s shipped in seconds can take weeks to stabilize” should serve as a clear call to implement strict linting, comprehensive test coverage and architectural reviews for all AI-generated code. 

Beyond the risks in generated code quality, the sheer speed of AI experimentation fuels another concern: The rise of ‘shadow AI APIs’. These endpoints are often spun up quickly by teams to test a new AI model or workflow, frequently bypassing standard governance. Awasthi describes these shadow AI APIs as posing ‘dual risk’. He explains, “Firstly, they bypass traditional API governance. Secondly, they become part of AI agent workflows that continuously invoke them. This isn’t just shadow IT – it’s living infrastructure stitched together by AI in real time,” which may perform critical tasks yet lack observability, access controls or formal ownership. It’s essential, then, to extend discovery efforts to these experimental zones, ensuring even rapidly created APIs are identified and assessed for risk. 

Chandrakanth Puligundla, a data analyst at Albertsons Companies, adds that these shadow AI APIs “often skip normal approval processes, which can lead to problems with data privacy and compliance.” He contrasts a key difference from past issues: “Unlike traditional shadow IT, which involves unauthorized hardware or software, shadow AI APIs can be hidden within existing systems, making them harder to find and manage.” This increased stealth means that proactive discovery and clear policies for AI experimentation are more critical than ever to avoid compliance breaches and data leaks. 

Can Yesterday’s Rules Manage Tomorrow’s APIs? 

The proliferation of AI-driven APIs puts immense pressure on organizations to manage these digital assets and secure their escalating attack surface. When APIs can be generated in minutes, and AI agents begin interacting with them in dynamic and often unpredictable ways, the old rulebooks start to look outdated. If governance can’t keep up, the risks of security breaches, compliance failures and operational instability multiply rapidly. 

From his engineering experience at AWS, Pandey is direct in his appraisal that current API governance frameworks and tools are ‘not really’ well-equipped for this new reality. He points out that “most tools were built for service catalogs and manual workflows. They lag in dynamic environments where APIs are spun up by tools, not humans.” To avoid what he calls “governance in reverse after something already gone wrong,” organizations urgently need to adopt “real-time API discovery, policy-as-code and automated lifecycle hooks.” This shift toward automation and real-time responsiveness is no longer a luxury but a necessity for effective governance. 

Awasthi (of Twilio) highlights the inadequacy of current tools for AI agent consumption patterns. “Most governance tools were designed around human-led development and consumption patterns,” he says. “With agents in the loop, APIs are consumed in unpredictable ways — at higher velocity, frequency and variability… Traditional tooling can’t reason about intent, context or adaptive behavior.” His call for “API governance that can operate at runtime — enforcing dynamic guardrails, detecting misuse heuristically and understanding agent intent” underscores the need for governance systems to become as intelligent as the AI they aim to manage. 

The very pace of AI development can make older governance models obsolete, as Pinkhasov explains. “Most governance frameworks were built for human-paced development cycles; monthly release cadences, manually written Swagger documents and Git-based approvals. That model collapses under AI-accelerated velocity.” He sees the next evolution requiring “AI-native observability, intent-based access control and self-updating registries; not static repositories of documents no one updates.” Businesses must therefore look toward adaptive governance solutions that can match the dynamic nature of an AI-driven API landscape. 

Adding a layer of real-world pragmatism, Akash Agrawal, VP of DevOps & DevSecOps at LambdaTest, notes that even where “fairly good governance frameworks and tools to check APIs exist, they often get violated by process due to the nature of go-to-market (GTM) speed.” This crucial insight means that, alongside upgrading tools and frameworks, organizations must also foster a culture where adherence to governance isn’t seen as a GTM blocker but as an enabler of sustainable, secure innovation. 

You’re Flying Blind If You Can’t See Your AI APIs 

With all these new APIs emerging at an unprecedented pace, how can any organization hope to manage, govern or secure what it doesn’t even know it has? 

AI systems don’t operate in a vacuum. They interact with a multitude of APIs, often in novel and complex chains, and AI tools are now also generating new APIs that can easily get lost in the shuffle if not actively tracked from inception. So, the need for clear sight is the absolute starting point, the foundational ‘step zero’ before any effective AI governance or API management can truly begin. 

It has been said that you can only govern the APIs you know about, and this could not be truer as AI expands its role in organizations. Voget explains that historically, getting a complete API inventory has been a challenge, “but new technologies (such as Blackbird) are removing that roadblock with repository integration and scanning and even automatic specification generation from code.” For any business serious about AI, actively investing in modern discovery solutions that reveal the entire API landscape — including these new AI-generated assets — is no longer optional. 

This principle is a cornerstone of cybersecurity. There’s a well-established pattern in cybersecurity when new technologies reach a critical mass for adoption, and it always starts with visibility. Tim Erlin, security strategist, VP of product at Wallarm, emphasizes, “whether we’re talking about server, virtual machines, cloud, containers or APIs, the path to security best practices always starts with a complete and accurate inventory.” Therefore, establishing and maintaining this complete API inventory is the baseline from which all effective security and governance practices must be built, especially in the fast-moving AI domain. 

When AI agents begin to actively use APIs as tools, this need for visibility becomes even more acute. Awasthi asserts that in such scenarios, “visibility becomes non-negotiable. You need to know which APIs exist, what they do, who or what is invoking them and how they’re behaving.” He warns that without it, “you’re flying blind in an environment where actions are being taken autonomously and at scale.” This highlights the critical need for real-time discovery and monitoring systems that can keep pace with autonomous agent activity. 

The dynamic and often poorly documented nature of AI-generated or AI-consumed APIs means that traditional, manual discovery methods simply can’t keep up. This is where more advanced

techniques become essential. Agrawal offers a stark perspective, “The major problem over here will be discovery of the API… security of the API is never a problem, it’s always a discovery. If you discovered that you will be fixing it out.” He recommends technologies like eBPF, which LambdaTest utilizes, for “a kernel-level interception of all the network calls,” as a way to achieve comprehensive discovery, especially when AI accelerates API creation “beyond human oversight.” Exploring and adopting such modern, automated discovery mechanisms is therefore crucial for any organization looking to truly get a handle on its AI-driven API ecosystem. 

So, How Do You Build an API Strategy That Actually Works With AI? 

To turn the API explosion into a managed asset rather than a runaway risk, organizations must fundamentally evolve their strategies, development practices and the tools their teams rely on. 

The way software, and specifically APIs, are built is already changing — with AI assistants becoming integral to the development workflow. This shift means re-evaluating how quality is ensured, how trust is established in AI-generated code and how the entire development lifecycle can be adapted to harness AI’s speed without succumbing to its potential pitfalls. 

It calls for reform to account for an API ecosystem that is far more dynamic, decentralized and increasingly consumed by non-human actors like AI agents. Yesterday’s static API strategies won’t cut it; what’s needed is a resilient and adaptive approach that can keep pace with AI-driven innovation. 

Voget envisions this evolution clearly, noting how “each developer now has a super powerful AI sitting right next to where they code — Cursor or Copilot are examples.” While AI is “completely underutilized by developers right now,” he anticipates that as it “gets more sophisticated, developers will start to use AI to offload some of the more tedious or repetitive development tasks.” Critically, “they will still need a place to test out the changes.” This points to the need for organizations to invest in tools and platforms that not only help developers trust AI-generated outputs like specs and code but also provide accessible environments for validating these AI-assisted creations, streamlining a new inner development loop of “Ask AI → Review → Run → Test → Repeat.” 

This need for thoroughness with AI-generated assets is echoed by Pinkhasov of Chronos. His advice is to “Treat AI-generated APIs the same as hand-coded infrastructure.” This means applying “automated testing, peer review (even if the author is ChatGPT), dependency auditing and CI/CD hooks,” and importantly, to “implement anomaly detection at the API layer, so you can catch misbehaving endpoints before they scale into technical debt.” Adopting such disciplined practices is essential to ensure that the speed gained from AI in development doesn’t come at the cost of quality or security. 

Building a resilient API strategy also requires a shift in mindset. Pandey advises organizations to “assume every AI experiment will hit production eventually. Build guardrails early — API versioning, monitoring and access control — even for internal or ‘temporary’ endpoints.” His core principle, “if it has an interface, treat it like a product,” should become a mantra for any team building or managing APIs in the AI era, fostering a sense of ownership and responsibility. 

Awasthi takes this further, suggesting we “treat APIs not just as code, but as operational contracts — especially for those used by agents.” A future-proof strategy, in his view, “must account for human and non-human (agent) consumers,” which means end-to-end API discovery, real-time usage introspection and policy enforcement that’s flexible and programmable. He even suggests that APIs should “declare capabilities in a machine-readable way (OpenAPI isn’t enough), and systems should simulate, verify and sandbox agent behaviors before exposing critical functionality,” calling this level of diligence “survival” in an agent-driven world. 

Reinforcing this proactive stance, Agrawal of LambdaTest recommends that organizations “expand API discovery to include the model serving interface to detect the AI-specific APIs and classify them,” and critically, to “apply zero-trust to all APIs, particularly those interacting with LLMs.” This focus on zero-trust and specialized discovery for AI interfaces highlights the need for security to be deeply embedded and adaptive within any future-proof API strategy. 

Turning the AI API Tide from Risk to Reward 

The AI-fueled API explosion is undeniably reshaping our technological landscape, but this rapid proliferation doesn’t have to lead to unmanageable chaos or stifled innovation. With a proactive and informed approach, organizations can confidently navigate this new era. 

Voget offers a crucial piece of parting advice, “Before breaking ground on AI implementation, tech leaders need to conduct a thorough site survey — mapping their data terrain, testing organizational soil and grading their systems for what’s to come.” This foundational diligence is key. 

Ultimately, transforming the potential risks of an API explosion into a powerful engine for AI acceleration hinges on one critical capability: Comprehensive visibility. This is a clear line of sight into the entire API ecosystem that enables the creation of a stable architectural foundation, allowing AI to drive business value safely and at scale, turning potential liabilities into your company’s next wave of innovation.