Your Agent Is Only as Good as Your Quality Stack
Here's the scenario that I read daily: I asked an Agent to do something, but it didn't do it quite like I'd hope.
And the irony is
Building and Storing Agent Skills
Extendable capabilities within any Agentic framework is key to a quality and performant output of an Agent. Whether you're writing code, designing a new logo for your product, or architecting a
Route and Secure OpenAI Azure Foundry Traffic Through Your AI Gateway
As you begin to expland into various Agentic frameworks, there's a good chance you will end up choosing the one that exists within the cloud provider you're already using.
Prompt Enrichment with Agentgateway: Injecting Context at the Gateway Layer
Quality output from an LLM is the make-or-break between a task that performs well and a hallucination. The level of accuracy that's output is top of mind for everyone using Agents.
Intercept, Inspect, Secure: Proxying Claude Code CLI Traffic
Architecture diagrams always look something like this:
Agent -> Gateway -> LLM (or MCP Server).
The Agents that organizations are typically referring to are Agents that perform an action via prompts