Systems | Development | Analytics | API | Testing

The era of simple, scripted AI is swiftly fading. We’re now witnessing the dawn of AI Agents: sophisticated, self-governing digital entities that possess the capacity to comprehend their surroundings, navigate intricate problems, and execute purposeful actions. Multi-agent systems take this even further, multiplying these capabilities by enabling teams of AI agents to collaborate, delegate tasks, and solve challenges collectively in ways a single agent cannot achieve alone.

Visualize launching a new AI chatbot for your business. It’s supposed to be perfect. But on day one, it recommends out-of-stock products, gives wrong order updates, and even provides wrong pricing information. Confusion spreads, support tickets pile up, and customers start to leave. It’s not always the chatbot’s intelligence, it’s the lack of testing before and after launch.

Large Language Models (LLMs) are transforming how organizations interact with their data infrastructure, offering unprecedented capabilities for both technical and business users. However, this transformation brings unique opportunities and challenges that vary significantly based on user personas, security requirements, and implementation approaches. This writeup explores these dimensions through the lens of practical implementation using tools like Keboola MCP and various client interfaces.

The rise of Model Context Protocol (MCP) has given AI agents and large language models (LLMs) a standardized way to talk to external tools, APIs, and data sources. In theory, it solves the messy integrations and custom connectors that have slowed down real-world agent adoption. A clean protocol should mean smooth interoperability. However, we’re observing certain patterns of fragmentation. Each MCP server runs in isolation. Agents have to handle multiple connections.

Today, we’re announcing that Kong has acquired OpenMeter, the open source and SaaS leader for real-time usage metering and billing. OpenMeter’s capabilities will be integrated into Kong Konnect, enabling usage-based pricing, entitlements, and invoicing for APIs, events, and AI workloads. This is a huge milestone for Kong, and we’re excited about what this means for our customers and the future of how you build and scale revenue-generating digital products for the agentic AI era.

You didn’t miss a requirement. You just built a strategy for the wrong kind of system.

Agentic AI is transforming how businesses operate by enabling systems to handle complex tasks autonomously. Instead of relying on constant human input, these AI systems break down high-level goals into smaller tasks, make decisions independently, and improve continuously through feedback. Here's what you need to know: Key Features: Autonomously manage workflows and processes. Handle multi-step decision-making and problem-solving. Learn and adapt based on performance data.

As QA teams scale, getting the right data at the right time becomes critical. Yet too often, it’s buried in folders, slowed by clunky filters, or locked behind SQL queries causing wasted time, delayed cycles, and inconsistent reporting. But moving as fast as we are today, you can’t wait on reports or guess where a test case lives. You need answers now. With QMetry’s Smart AI Search and AI-powered SQL generation, QA teams get instant access to data in natural language. No hunting.

Integrating AI into enterprise systems is a high-wire act: you must deliver value quickly—without breaking security, compliance, or scalability. This guide distills security-first patterns CISOs can operationalize immediately: zero-trust for every AI interaction, least-privilege RBAC, end-to-end encryption and secret management, auditable-by-default pipelines, and a platform approach that minimizes custom code and speeds delivery. Bottom line: Treat AI like any external, untrusted client.