In the age of AI, there is a worrying trend of simply letting AI "take care of it." You have invested in an agentic system, so when you need something done, why not just let the AI agent make the API request? After all, it is just a machine making a machine request — right?

Unfortunately, it is not that easy — and that mindset can lead to some significant risks. When you let AI make your API requests, you can get some huge benefits, but you can also introduce significant financial risks, security issues, accuracy concerns, and performance overhead that, at best, can make the system inefficient — and, at worst, can undermine your entire organization.

Today, we're going to dive into six big risks of letting AI make API requests. We'll look at some of the dangers of direct API execution by AI agents and offer some architectural solutions that can mitigate these impacts.

1. Runaway Cost Amplification

One major risk in this paradigm is intrinsic to the nature of how AI — and APIs — work. LLMs consume tokens. APIs consume billable units. When everything works the way you expect it to, this cost can be balanced, resulting in more effective use of resources and a shorter time to value.

But hidden in that potential upside is the fact that leaving AI and API costs to a system that can hallucinate, can face technical breakdowns, or simply can fail to do the tasks as defined, can introduce high costs. Infinitely looping costs can kill your token pool and generate extreme infrastructure costs. Unbounded retries can cause a simple technical glitch to generate costs in the hundreds of thousands of dollars. A single expensive call can be chained to other expensive calls, resulting in those cost savings promised by your AI system evaporating into thin air.

The reality is that these systems can generate runaway costs with frightening ease. It's hard to imagine an engineer looking at an incredibly expensive call that fails and simply saying, "let's retry that 100 times" — but for an AI, that's unfortunately easy and common.

• The fix: The best way to address these issues is to apply infrastructural limits. Implement hard rate limits, establish maximum budgets (both through soft limits in AI instructions as well as hard limits in maximum billing amounts), establish hard token-based execution limits, and put in place strict guardrails to prevent looping retries and calls.

2. Collapsing Context and Hallucinated Contract Calls

Providing AI with context is essential, but at scale, LLMs lose fidelity as their context grows. This can lead to hallucinations and missing details. According to AI-monitoring provider Galileo, "in RAG pipelines, noisy embeddings can reduce retrieval accuracy by 20–30%." The more you rely on your AI system, and the more data you introduce, the more likely it is to cause confusion.

When you let your AI agents do everything with your APIs, errors start to accelerate. Suddenly, you begin seeing incorrect parameter formatting, use of deprecated endpoints, fully invented data fields, and incorrect assumptions. This is the very nature of an LLM — and it requires significant architectural and human guardrails to ensure that this does not balloon. API agents require perfect contract alignment, but LLMs are, at their core, statistical guesswork.

• The fix: To resolve the problem of collapsed context, you will need to be overly explicit with the underlying systems. By establishing static capability schemas, ensuring strict validation, and establishing a solid example or demonstration modality that you can point the LLM to, you can close the distance between what the LLM thinks you want from your API calls and what you actually want.

3. Fragile Authorization and Identity Misuse

Another huge risk intrinsic to this process is the fact that LLMs do not inherently understand — or often, respect — fundamental authorization and identity processes. LLMs do not understand the concepts of least privilege, resource boundaries, or multi-tenant data constraints. While you can set up systems to clarify this, the LLM is not interested in respecting them — it is interested in getting you an answer.

In this reality, one poor prompt can lead to privilege escalation, regulated data exposure, or fundamentally broken requests. This is not theoretical — it's based on observed data collected by projects such as the OWASP Top Ten for LLMs project. For instance, AI security provider Equixly found that 43% of MCP servers contained command injection flaws — a worrying data point considering that 70% of vendors with these issues either ignored the security disclosure or responded that the attack vector was theoretical.

In essence, this is like giving a junior developer the keys to the kingdom — and then asking them to do something for you regardless of the systems in place. This can lead to significant security gaps, and the only solution is to limit what the AI can actually do.

• The fix: Establish strict standards and systems, and then ensure that your AI is boxed within those systems. For instance, strictly scoping service accounts, enforcing Open Policy Agent (OPA) and attribute-based access control (ABAC) limits, and limiting access tokens for AI systems to only those of the least privileged user class will sidestep the worst scenarios. There is a tack-on effect here to keep in mind: weaknesses in your general posture can be used by the AI agent, so you will need to be holistic.

4. Non-Deterministic Behavior in Critical Paths

Traditional API workflows rely on predictable execution — but LLMs are probabilistic, and at times stochastic. That means the same request chain made today may behave significantly differently tomorrow — in terms of business logic, order of operations, interpretation of ambiguity, and even ultimate outcome format.

This can wreak havoc on your systems, but it can also make for unreliable business processes, since the unpredictable nature of AI can cause data formatting issues. You can get around this, but in many cases, the question becomes whether you are putting more effort into the formatting than you are saving by handing the task off.

For this reason, you need to ensure that you are balancing the strictures while also understanding which tasks no longer lend themselves to efficiency — and instead amplify overall cost.

• The fix: To mitigate this issue, you will need to put in place strong guardrails to ensure that multi-step agent-driven workflows are at least in alignment with your general API flow. You can use systems like the Arazzo Specification to define these workflows, leveraging serialized API calls to create chokepoints for certain processes where needed. This requires deterministic orchestration layers, human-reviewed checkpoints, and end-state goalposts, which allow you to retain the benefits of the agentic flow while limiting randomness.

5. Poor Error Recovery and Fault Attribution

One issue that arises when you give AI more control is a question of responsibility. If an engineer makes a poor call, you can trace that call and figure out what got you to that state. If an API makes a poor call to another API, you can follow the schema and identify the how and why. But what do you do when an AI hallucinates and makes a request? How can you drill down into the details to determine root causes?

Fundamentally, the issue here is one of error recovery and fault attribution. When something breaks, who is responsible? Was the model wrong? Was the prompt ambiguous? Or was the API at fault — for instance, did its latency and poor error reporting cause retry flooding?

When you give AI access to APIs in an uncontrolled way, failure modes become harder to isolate, reproduce, or monitor — causing issues that are harder to resolve at scale.

It's important to note here that this is an ever-evolving frontier issue, but there is a large body of legal precedent suggesting that, in lieu of fault attribution, the default responsibility may fall upon the implementer. In other words, absent any AI liability law, those who implement these AI flows — or fail to properly implement them — are just as liable as they would be in any product negligence case.

• The fix: To address poor error diagnosis, you will need to proactively build structured tracing systems, as well as agent-decision logs, standardized error systems, and clear attribution mechanisms. Only by doing this can you ensure that errors arising from AI weaknesses are properly attributed and rectified.

6. Data Leakage and Authorization Boundary Breakdowns

Even abstracting the immediate foundational problems with giving AI access to APIs, there is one major concern in terms of blast radius — when an LLM can access real data, the potential fallout drastically expands.

Consider an AI that can access an API with private or secure data hosted on a multi-tenant service. What happens if the AI leaks data? And what does that leakage look like? In an agentic access paradigm, you lose much of the understanding that comes from human operators. As a result, you might see data surfaced that would not typically be exposed.

This can take a variety of forms, including the retrieval of sensitive fields in real data, the accidental combination of cross-tenant records and data sources, and even the "learning" and reuse of critical personally identifiable information (PII). This can also compound issues already seen in the wild. For instance, Clutch Security has found that 38% of MCP servers are unofficial implementations, which mirrors shadow API and API sprawl issues that are still painfully omnipresent in the industry.

• The fix: There are several ways to address this, but the approach will heavily depend on your data structures. Common solutions include attribute-level masking such as k-anonymity, system-wide encryption such as fully homomorphic encryption, redaction gateways that strip PII from returned data, and separate vectors for operations, memory, and context storage.

Potential Architectural Fixes

Many of the issues of letting AI make API requests arise from a simple architectural problem — the lack of supervised execution. The way AI systems work means there are several stages between the user request and the agentic response. With a highly stochastic and probabilistic system like an LLM, this introduces multiple potential failure points. Put simply, trusting raw API execution rights to an LLM is like giving an intern a corporate credit card without any spending limits and hoping they can read — or even find — the corporate spending handbook.

To resolve this larger issue, you need to adopt a supervised execution architecture. This involves creating a layered system where each layer has specific limitations and orchestration elements that ensure intent is converted into safe procedures. Add a policy engine — an orchestrator of orchestrators that enforces security, spending, and governance rules — and you can ensure safe building and consumption.

Preventing AI From Eroding Cost and Trust

LLMs are not going away any time soon, and their active calling of APIs will be a major part of the next decade. The teams that truly benefit — and notably avoid the negative side effects of this technological shift — will be those that build observability, spending controls, and security as a base layer from day one. Absent these controls, LLMs are liable to run rampant, costing untold amounts of money and eroding user trust along the way.