The API-Hungry, Agentic AI Era: How AI is Learning to Take Action

As we approach year-end and feel excited about 2025, one thing is common: the excitement for Agenticai can hardly be controlled. Hardly a day passes, be it any social media, and the buzzword for 2024-2025 can be missed. Perhaps we might miss a heartbeat but not Agenticai.

This shift towards "agentic AI" marks a new chapter in the evolution of artificial intelligence, but agents need to communicate with other agents or call models for various purposes. By connecting to APIs, these agents can book flights, schedule appointments, analyze real-time data, and execute trades—all while maintaining natural conversations with users.

The API Economy Meets AI

As these systems become more sophisticated, we'll likely see them take on increasingly complex tasks, coordinating across multiple services and domains. The key will be balancing their autonomy with appropriate oversight and control mechanisms. Most companies today have a fairly robust API model and lifecycle management and will now need to extend to agents. However, intelligence comes with its own set of challenges that APIs need to deal with in terms of security and user trust that will shape the next generation of AI applications.

Corporate environments are seeing AI assistants, not to mention co-pilots, that don't just participate in meetings but automatically update project management tools, schedule follow-ups, and trigger workflows across multiple systems, and we have also seen cases of hallucinations and snooping into areas not permitted as well.

Comprehensive Risk Analysis and Mitigation Strategy

1. Fragmented Knowledge Landscape

When APIs become undiscoverable, we see:

• Duplicate APIs being created for identical functionalities
• Inconsistent versioning across different teams
• Shadow APIs that exist outside official documentation
• Conflicting standards and implementations

2. Resource Inefficiency

The hidden costs manifest as:

• Multiple teams solving the same problems
• Increased infrastructure costs from redundant services
• Higher maintenance burden across duplicate systems
• Wasted development effort on existing solutions

3. AI Agent Limitations

Undiscoverable APIs severely impact AI agents by:

• Restricting their ability to discover new capabilities
• Creating inconsistent interaction patterns
• Limiting their ability to adapt to new services
• Forcing reliance on outdated or suboptimal APIs

The cascading Nightmare

Technical Debt Acceleration and Effects

• Every Enterprise architect nightmare is the dreaded word, and I have been constantly shouting about the need for a Chief Cleanup Officer (no V.C. is interested in it).
• Threat vector expansion and more jobs for cybersecurity
• Degraded AI agent performance as agents will compete with the same or similar API that could result in suboptimal performance and loss of trust in the agent itself.
• Unpredictable costs and a constant worry for IT Finance Management.
• Data Privacy and Security Mitigation

Operational Risks

• System Overreach
• Error Propagation & Resolution
• User Trust and Adoption Mitigation

Business Risks

1. Cost Management
2. Vendor Lock-in

As with any new challenges and opportunities, good governance comes into play.

Future-Proofing Strategies

Architectural Considerations

• Design for discoverability from the start ( Understand and Implement Design thinking and security from the start)
• Implement semantic versioning
• Create self-describing APIs ( Strong API Discovery and Lifecycle Management policies)
• Build with automation in mind

2. Documentation Evolution

• Real-time documentation updates
• Interactive API explorers
• Automated testing of documentation
• Version-aware documentation systems
• Ensure the build and devsecops are consistent

3. AI Agent Adaptation

• Implement dynamic API discovery capabilities
• Create fallback mechanisms for unavailable APIs
• Develop API preference learning
• Build API health monitoring systems
• Utilize API Gateways

Moving Forward

The challenge of API sprawl requires a multifaceted approach combining technical solutions, organizational changes, and new methodologies. There is enough literature on API strategy for enterprises and it is now crucial for the success of agentic AI. This era depends on our ability to make APIs discoverable, maintainable, and efficiently usable by both human developers and AI agents. We will co-exist and be more successful