As AI shifts from answering questions to taking action, we’re entering the age of Agentic AI—AI assistants that plan trips, coordinate purchases, or even book apartment viewings by stringing together multiple APIs in real-time.

The technical foundation? API orchestration at scale.

Agentic AI systems, like those emerging from Project Mariner and other multi-agent initiatives, rely on deep integration with external tools and services via APIs. But with great power comes complexity: each interaction is an API call, and orchestrating these at runtime introduces a set of high-stakes challenges.

The Rise of Agentic AI (And the API Complexity It Introduces)

Imagine an agent tasked with planning an international trip. It needs to:

Query flight APIs
Compare hotel prices
Check weather conditions via a weather API
Book reservations through calendar and messaging APIs
Initiate payment securely through a finance API

Behind the scenes, this involves tens to hundreds of API calls, across different providers, protocols, data formats, and latency profiles.

The challenge? Managing all of this securely, reliably, and with observability.

Let’s break down the six critical API orchestration pain points Agentic AI developers face—and how digiRunner Open Source addresses them better than other OSS alternatives.

1. API Discovery and Integration Complexity

The Problem: Agents need to know what services are available and how to interact with them. Lack of standard metadata or self-describing APIs increases integration friction.

How digiRunner OSS Helps:

Built-in admin console with API categorization and tagging
Support for OpenAPI and AsyncAPI standards
Central API catalog to simplify agent integration logic

Compared to:

K-OSS requires third-party plugins or enterprise add-ons for discovery.
G-OSS lacks real-time API auditing for unknown/rogue services.

Best Practice: Use digiRunner’s API Discovery and Registry features to audit all available services before exposing them to agents.

$digiRunner admin console simplifying API discovery and integration for agentic AI$

2. Security and Access Control for Agents

The Problem: Agents often operate on behalf of users. They must access sensitive APIs while protecting against threats like indirect prompt injection.

How digiRunner OSS Helps:

Full support for OAuth 2.0, OIDC, API Keys
In-memory secrets management (no credentials in files)
JWS/JWE encryption to secure payloads
Configurable policies to sanitize inputs and outputs

Compared to:

K-OSS supports basic authentication but lacks in-memory vaults in OSS.
G-OSS has basic policy enforcement but lacks fine-grained injection protection.

Best Practice: Leverage digiRunner’s policy engine to create a perimeter around AI agents, ensuring they cannot be manipulated by tampered responses or rogue data.

digiRunner enforcing OAuth2, OIDC, and encryption to secure agent API access

3. State Management Across Long-Running Conversations

The Problem: Agentic tasks span multiple steps and APIs. They must retain memory—both data and intent—across calls.

How digiRunner OSS Helps:

Custom context propagation headers between APIs
Orchestration support to maintain in-session state
Built-in retry logic for long-latency calls

Compared to:

K-OSS lacks stateful orchestration in OSS.
G-OSS supports workflows, but not with conversation context chaining.

Best Practice: Use digiRunner’s routing rules and header injection to propagate task context across workflows.

digiRunner maintaining context and state across long-running AI conversations

4. Resilience and Error Recovery

The Problem: What happens when one of the APIs fails? Agents must handle errors gracefully.

How digiRunner OSS Helps:

Circuit breakers and retry policies
Fault isolation via thread pools
Intelligent traffic shaping (Fast Lane vs General Lane) in v4.5

Compared to:

K-OSS supports retries but lacks intelligent traffic shaping.
G-OSS offers no built-in thread prioritization in OSS.

Best Practice: Apply Fast Lane policies in digiRunner to isolate high-performing agents from error-prone services.

digiRunner circuit breakers and traffic shaping ensuring agent reliability

5. Performance and Latency Optimization

The Problem: The total latency of an Agentic workflow is only as fast as the slowest API.

How digiRunner OSS Helps:

Parallel processing of independent API requests
Rate-limiting and traffic throttling
Built-in analytics for endpoint performance

Compared to:

K-OSS needs external observability stacks to analyze latency.
G-OSS has limited built-in analytics in open source.

Best Practice: Use digiRunner’s real-time API dashboards to optimize slow endpoints and set SLAs on performance.

digiRunner parallel processing and real-time analytics to reduce API latency

6. Observability and End-to-End Debugging

The Problem: When an agent fails, developers need to understand where—fast.

How digiRunner OSS Helps:

Live traffic heatmaps and real-time API logs
Integration with error dashboards
Historical reports by API, consumer, or use case

Compared to:

K-OSS relies on Prometheus/Grafana integration (extra setup)
G-OSS lacks deep request tracing capabilities

Best Practice: Enable live logging and anomaly alerts in digiRunner to catch issues before they impact agent behavior.

$digiRunner real-time monitoring with traffic heatmaps and API error tracing$

Why digiRunner OSS Stands Out in the AI Era

Unlike K-OSS and G-OSS, which focus on traditional gateway routing, digiRunner Open Source is engineered for modern AI-first workflows. Its built-in capabilities—like intelligent traffic shaping, adaptive MFA, JWT/JWE security, and real-time dashboards—make it the ideal choice for prototyping and evaluating Agentic AI applications.

In summary:

Feature	K-OSS	G-OSS	digiRunner OSS
Agent-Oriented Routing	❌	❌	✅
Intelligent Threading (Fast/Slow Lanes)	❌	❌	✅
API Discovery + Shadow API Detection	❌	⚠️	✅
In-Memory Vault	❌	❌	✅
Out-of-the-Box Observability	⚠️	⚠️	✅

Final Thoughts

Agentic AI isn’t just a trend—it’s becoming the new baseline for how services interact. And while building smart agents is hard, managing their API interactions doesn’t have to be.

digiRunner Open Source offers a production-grade evaluation platform for developers and CTOs building the next generation of AI services.

🔗 Explore digiRunner on GitHub

📚 See documentation at OpenTPI

🌐 Learn more at OpenTPI

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Start evaluating today—and turn your smart AI agents into reliable, scalable business solutions with the right API management foundation.

Behind Every Smart Agent Is Smarter API Management: Orchestrating Agentic AI with digiRunner OSS