You've diagnosed the problem. Your agentic AI initiatives are stalling — not because the models are wrong, but because the integration layer underneath them wasn't built for this. Batch data, rigid schemas, fragmented governance, no real-time event delivery. The plumbing is the bottleneck . Now the…
Recent research from Forrester’s David Mooter makes a few things very clear: the enterprises that win with agentic AI won't be the ones with the most agents — they'll be the ones with the best integration infrastructure behind them. If you've been following the agentic AI wave, you've probably…
You would never expose a REST API without an API gateway in front of it. Authentication, rate limiting, observability, access control — these aren't optional extras. They're mandatory, and we've spent the last decade building the API gateway pattern to solve exactly this problem. So here's the…
As APIs grow across teams, keeping them consistent becomes difficult. Some APIs follow naming conventions and include clear descriptions, while others don’t. Over time, these differences make APIs harder to understand, review, and maintain. That is where API linting helps. Kong Insomnia has already…
We're thrilled to announce a strategic technology partnership between Kong and ModelOp . As enterprises rapidly transition into the agentic era, they face a critical challenge: how to deploy AI fast enough to stay competitive without taking on unacceptable regulatory or security risks. Together,…
If you're running Kafka at any real scale, you've probably hit some version of these three problems. Compliance wants specific fields — SSNs, emails, account numbers — locked down, but your consumers still need the rest of the record. Your topic names are load-bearing in ways nobody planned for, so…
Gartner recently published its inaugural Magic Quadrant for AI Governance Platforms (AIGP) — a signal that the AI governance category has graduated from analyst speculation to something enterprises are actively buying. Thirteen vendors were formally evaluated. The market is projected to grow from…
Shadow AI detection is the practice of finding and governing unsanctioned AI tools, models, and API integrations that employees deploy without security approval. It has become urgent because these tools route live enterprise data to external models in real time, and traditional security stacks…
The MCP demo impressed the room. Then someone asked how 5,000 employees would connect to 40 MCP servers, and the answer was: one OAuth consent screen at a time. Per user. Per server. No central policy, no unified audit trail, and nothing stopping a personal account from getting wired into a work…
AI adoption is accelerating. Governance is not keeping pace. According to Deloitte research, 74% of organizations plan to adopt agentic AI within two years, yet only 21% have a mature AI data governance model in place [1]. The gap between ambition and readiness is showing up in production metrics:…
AI has moved from the pilot phase into production, where it approves loans, screens patients, and routes customer traffic in real time. That shift raises the stakes: a model that misbehaves is no longer a demo problem, it's a business risk. AI governance is the control layer that keeps those…
This is the second post in a series. For the first part, see Why We Need to Stop Prompt Hacking . Generative AI systems do not fail because models are weak. They fail because architectures are incomplete. Once organizations accept that prompts cannot guarantee reliability, a new challenge emerges:…
Generative AI has completely changed the landscape of enterprise automation, knowledge work and operational efficiency. In 2026, the question is no longer whether these models can perform complex tasks, but whether they can do so reliably enough for mission-critical systems. Despite the…
Enterprises today manage thousands of APIs — and that number is growing fast. But APIs are no longer the only traffic flowing through your infrastructure. AI is adding entirely new categories of traffic: LLM calls to large language models, MCP requests that let AI agents access tools and data…
Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own…
Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a…