Get insights from leaders changing business through API innovation.
In large organizations, platform architecture tends to be more historical artifact than intentional design. At the foundation of the stack sit monoliths that predate modern API standards; many of them are too risky or too costly to decommission fully. Built on top of them are services that were…
Gartner's strategic planning assumption: by 2029, 50% of software application providers will be forced to share their context layer externally for third-party orchestrators to stay relevant. Today, that number is less than 2%. This isn't a gradual evolution. It's a structural shift in where value…
Gartner's strategic planning assumption stops you in your tracks: by 2029, 50% of software application providers will be forced to share their context layer externally for third-party orchestrators to stay relevant, versus less than 2% today. Let that number sink in. Nearly every enterprise…
When teams resort to VPC peering or PrivateLink to expose Kafka, they're not solving the problem — they're managing it, one network topology decision at a time. Every new external consumer adds complexity: a new peering connection, new firewall rules, new IP space to coordinate, new potential for…
This is the pitch to the board and the C-suite. It must be brutally concise, focused entirely on your business outcomes, not the technology. If the first page doesn't articulate value, the strategy dies. It immediately frames the initiative in terms of shareholder value and competitive advantage.…
AI spending is exploding across the organization—but often not in the ways leadership approved or finance can track. Development teams spin up LLM connections to ship features faster. Data teams provision GPU clusters for experiments that get abandoned. Multiple teams solve the same problem three…
Let's be honest about what's happening inside most enterprises right now. Development teams are under intense pressure to ship AI features. The mandate from leadership is clear: move fast. And so they do. They spin up LLM connections, integrate third-party AI tools, and route data to models without…
The report identifies a mindset trap that's holding most organizations back: "inside-out" integration thinking. Inside-out means viewing integration from the perspective of only prioritizing the reuse of legacy integrations and architecture (i.e., simply wrapping existing integrations with MCP and…
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: The goal isn't to replace existing development workflows but to extend them. Developers should be able to build agents using familiar paradigms while…
APIs are the connective tissue of digital products and services, and they're the lifeblood of AI. APIs shape customer experiences, power partner ecosystems, and accelerate enterprise innovation. As organizations double down on API-first strategies, one role is emerging as indispensable: the API…
The challenges of an acquisition frequently appear in a number of critical areas, especially when dealing with a platform as important as Kafka: Many organizations consider maintaining direct integration with their Kafka provider. However, compared to a direct coupling approach, an abstraction…
Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents. LLMs are being deployed, agentic workflows are getting created left and right, and the promises of what this technology…
The Requirement : Article 10 of the EU AI Act mandates strict data governance for high-risk AI systems. This includes error detection, bias monitoring, and arguably most critically for enterprise use — ensuring that sensitive personal data (PII) is not improperly processed or leaked into public…
A close examination of what really powers the AI prompt unveils two technologies: the large language models (LLMs) that empower agents with intelligence and the ecosystem of MCP tools to deliver capabilities to the agents. While LLMs make your agents smart, it would be hard to provide agents with…
Everyone's telling you to innovate faster with AI. Move quicker. Ship more features. Deploy more agents. But before we sprint headlong into the AI revolution, we need to have a proper dollars-and-cents conversation that most companies are avoiding. Disclaimer: Statistics cited in this post were…
Secure your AI infrastructure with prompt guards, PII sanitization, and centralized governance. Control LLM costs with token-based rate limiting and semantic routing across providers.