With the rise of agentic capabilities, companies are increasingly adopting AI agents that can perform complex work autonomously. These agents rely on API integrations to fetch live data and take action, introducing complexities in API management that traditional solutions weren’t designed to handle.
Understanding AI agents and their need for API access
At their core, AI agents are software programs that interact with their environment to perform self-determined tasks in pursuit of predetermined goals. What distinguishes them from simpler AI applications is their ability to independently gather information and take action through API calls. Agents rely on APIs to interact with the outside world, retrieving real-time information and performing actions autonomously.
For example, use cases for an AI agent tasked with managing customer support might need to:
- Query a CRM API to retrieve customer information
- Access a knowledge base API to find relevant documentation
- Update ticket status through a helpdesk API
- Send notifications via messaging APIs
4 ways AI Agents struggle to manage API access
The ability to access information and take action using APIs makes agents increasingly powerful, but it also introduces a host of complexities. Traditionally, APIs are accessed by deterministic code written by developers, ensuring APIs are accessed in predictable ways. Since AI systems are inherently non-deterministic, there’s little ability to pre-define how APIs will be accessed. That opens the door to a set of new challenges:
1. Complex operations and error cascades
AI agents often need to perform operations that require multiple interconnected API calls. For instance, to gather comprehensive customer data, an agent might need to:
- First fetch a customer ID
- Then retrieve their account details
- Finally access their transaction history
Each step introduces a potential point of failure, and errors compound across the sequence. Even with 90% accuracy at each step, by the ninth consecutive step the cumulative effect can lead to a drop in overall accuracy of up to 39%.
2. Error handling complexity
AI agents struggle to handle the wide variety of API errors they encounter:
- Authentication failures
- Rate limiting
- Server errors
- Network timeouts
- Invalid data responses
Without sophisticated error handling, agents may misinterpret errors or fail to recover gracefully, leading to task abandonment or incorrect assumptions about data availability.
3. Limited visibility
When agents generate and execute API calls autonomously, you lose visibility into:
- Which APIs are being accessed
- Frequency of calls
- Success rates
- Performance metrics
- Cost implications
This lack of visibility makes it difficult to debug issues, optimize performance, and ensure compliance with organizational policies.
4. Governance and security concerns
Autonomous API access by AI agents raises serious security considerations:
- Risk of excessive or unauthorized API calls
- Potential exposure of sensitive data
- Authentication credential management
- Compliance with data privacy regulations
- Cost control and resource utilization
Solving these challenges with a consumption gateway
An API consumption gateway can help mitigate API traffic originating from AI agents in your architecture. It provides a central point for governing outgoing API traffic, handling common issues such as authorization and rate limits, and providing observability for API access. In effect, an AI gateway monitors API consumption when it acts as a proxy for all of your agents’ outbound API traffic.
Boomi’s AI gateway built for this purpose. Engineering teams of any size gain unified controls to manage, orchestrate, and scale API egress traffic across environments, without code changes.
Boomi’s egress proxy is agnostic to any API provider. Its UI management layer gives you full egress traffic observability and real-time controls for handling cost spikes or issues in production, all through a simple SDK installation.
The AI gateway offers solutions for quota management across environments, prioritizing API calls, centralizing API credential management, and mitigating rate-limit issues.
API consumption management within an AI gateway offers:
1. Centralized control and visibility
- Real-time discovery and monitoring of all API calls
- Detailed logging and transaction analysis
- Performance metrics and error tracking
- Cost and usage analytics
- API catalog management
2. Intelligent traffic management
- Client-side rate limiting to prevent API quota exhaustion
- Priority queuing for critical requests
- Automatic retry mechanisms with circuit breakers
- Caching to reduce unnecessary API calls
- Traffic smoothing during usage spikes
3. Security and authentication
- Centralized credential management
- Secure key and certificate injection
- PII and sensitive data screening
- Granular access control at API and endpoint levels
- Authentication flow handling
4. Error resilience
- Standardized error handling across different APIs
- Automatic retry mechanisms for transient failures
- Circuit breakers to prevent cascade failures
- Clear error reporting and diagnostics
Deploying an Agent Control Layer for API consumption control
A unique challenge with AI agents is their tendency to generate code that directly accesses APIs via well-known URLs. One way to introduce an API consumption gateway like Lunar.dev is to manually switch the URLs for API calls from their regular URL to the proxy URL. However, because agents may default to accessing APIs’ well-known locations, this approach can prove unreliable. You can ask the AI to comply, but there’s no guarantee it will, since AI is naturally non-deterministic.
This calls for a different approach: funneling requests through to the consumption gateway. There’s an easier way to accomplish this, by intercepting requests made to well-known API domains at the network layer and funneling them to the proxy instead.
This approach relies on your application running in a private network you control, whether that’s a data center or a private cloud environment. For simplicity, the walkthrough below refers to AWS’s VPC network and affiliated products, but it works similarly on Google Cloud and Azure with their respective products.
The core of this approach is to hijack DNS resolution on your private network and intercept requests made to API domains. Specifically, for any API domain your agent may use (for example, api.something.com), you create a private DNS record that points to the consumption gateway. So when the AI agent calls this URL, instead of resolving to the public API’s servers, it resolves to the consumption gateway, which proxies the request after running all applicable policies and modifications on it.
VPC-based installation approach
This deployment method uses DNS-based routing within your VPC to route API traffic through Boomi without requiring code modifications:
- Deploy the proxy: install the proxy on an AWS machine outside your VPC.
- Configure DNS zones: create custom DNS zones in Route53 for each API domain you want to route through Boomi.
- Manage certificates: create a custom Certificate Authority (CA), generate certificates for proxied APIs, and add the CA certificate to your service’s trust chain.
- Configure routes: set up Route53 records to direct API traffic to the Boomi proxy.
This approach offers several advantages:
- No code modifications required
- Infrastructure team manages implementation
- Works with “uncontrolled” workloads and legacy applications
- Supports both cloud and on-premises deployments
Implementation considerations
- API discovery: manually configure DNS records for known APIs your agents will access.
- Certificate management: ensure your CA is properly configured and trusted across all services.
- Failover planning: consider implementing DNS-level failover using Route53 health checks.
- Horizontal scaling: deploy multiple proxy instances for reliability and load distribution.
As AI agents become more prevalent in production environments, managing their API consumption becomes increasingly critical. Boomi’s AI gateway addresses the unique challenges of agentic API consumption while offering flexible deployment options for different architectural needs. Through its feature set and VPC-based deployment capability, Boomi helps organizations maintain control, visibility, and security over their AI agents’ API interactions without compromising the agents’ autonomy or effectiveness.
To see how Boomi helps you govern AI agent and API traffic, visit Boomi’s MCP platform page.