Every request returned 200 OK, the task still failed. This is the problem in one sentence. A single user intent now fans out into multiple model calls, tool invocations, MCP (Model Context Protocol) interactions, policy executions, retries, and agent handoffs. Each call can look healthy on its own…
AI agents are becoming increasingly capable, but their usefulness depends on what they can access. An agent that can reason through a customer support issue is of limited value if it can’t retrieve customer information, check an order status, or initiate a refund. Similarly, an IT operations agent…
A healthy API and a successful model call do not prove that an AI application produced a useful result. AI observability connects logs, metrics, and traces with evidence about behavior, retrieval, tool use, quality, safety, and token consumption. That combined view helps AI engineers explain what…
AI governance is an operating model organizations can use to keep AI systems accountable, safe, observable, and aligned with applicable requirements. It can combine decision rights, lifecycle processes, and technical controls across models, agents, tools, data, and APIs. [1] [2] [3] For enterprise…
Agentic AI governance is the structured management of the authority an organization hands to autonomous AI systems that plan and take action on its behalf. It covers what an AI agent is allowed to do, which systems it can touch, and how those permissions are monitored, limited, and revoked using…
Trace complete AI sessions across models, tools, agents, and infrastructure — and understand where performance, cost, and failures accumulate. Traditional observability was built around requests. A request comes in. A service processes it. A response goes out. You trace what happened in between.…
New AI cost management capabilities help organizations price, attribute, plan, control, and optimize AI spend across models, applications, teams, and agents. AI is getting easier to build with. Paying for it is getting harder to manage. As organizations move from AI experiments to production…
Give AI agents a governed way to discover and consume the models, MCP servers, agents, tools, resources, and skills they need — without manually wiring every connection. AI agents are becoming more capable. And as they do, they need access to more of the enterprise. A coding agent might need a…
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:…
Each change to Kong Gateway's codebase triggers a comprehensive test suite that runs more than 17,000 * 2 = 34,000 test cases among the two primary architectures (x86 and ARM) we support. This process takes about 23.5 hours on a single machine. But we don't wait that long. A large fleet of machines…
At TDX earlier this month, Salesforce announced Headless 360: every capability in the platform now exposed as an API, MCP tool, or CLI command. Co-founder Parker Harris put the thesis bluntly last month: "Why should you ever log into Salesforce again?" That isn't a marketing question. It's an…
Every few weeks, the AI-adjacent internet crowns a new corpse. SaaS is dead. OpenAI is dead. The IDE is dead. Humans writing code is dead. The structure is always the same: take a real observation, strip it of all nuance, and toll the death knell to feed the hype train. Rinse and repeat. The…
Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is very similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance…
Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait. In their…
A persistent challenge arises as businesses adopt multicloud architectures and agentic AI: the need for state synchronization. API and AI gateways require a robust persistence layer to synchronize data, whether it's for governing AI token usage, facilitating agent-to-agent communication, or…
APIs and integrations are the foundation of the modern enterprise. Every organization needs to securely connect systems, move data, and automate workflows, all while maintaining control, visibility, and flexibility. Increasingly, those same APIs are also being consumed by AI-powered applications…