OpenTelemetry

Multi-agentic architectures can pose challenges from an observability perspective. Each agent within a workflow makes its calls to its own toolset and each agent may be configured to use a different underlying LLM (or even a Model Router which picks the most efficient model for us). Being able to observe which agent made what call with which model in a unified manner can be challenging over an entire workflow. OpenTelemetry provides semantic conventions that help us solve this problem and Microsoft Foundry provides tracing integrations for agent frameworks that enable us to implement tracing for different agentic frameworks, and assist us in quickly identifying root causes for issues in our agents. ...

When building an MCP server for my side project (Biotrackr), one of the first things I had to think about was how to manage secrets. The MCP server needs an APIM subscription key to call downstream health data APIs, and it also exposes an API key for clients connecting to it. That’s two secrets that need to be stored, distributed, and protected and if either one leaks, the blast radius could extend across the entire platform. ...

Your AI agent depends on a chain of services. In my side project (Biotrackr), the chain looks like this: Claude API for reasoning, APIM for routing, downstream APIs for health data, and Cosmos DB for chat history. When one link in that chain fails, things can get ugly fast. Imagine this: Claude API returns a 429 (rate limited). The agent retries the same request. Each retry consumes more tokens. More 429s. The conversation times out. The user sees an error and submits again, doubling the load. A single rate limit hit has cascaded into a degraded experience, wasted tokens, and a frustrated user (in my case, just me screaming at my own agent 😅 For you however, that would be your customers!). ...

What happens when the agent itself becomes the threat? Not because of a prompt injection (ASI01) or tool misuse (ASI02), but because the Claude model produces systematically wrong analysis, the Agent Framework has a bug in its tool loop, or the Anthropic API starts returning manipulated responses? Throughout this series, we’ve covered controls that protect the agent from external threats (hijacked goals, misused tools, stolen identities, supply chain poisoning, code execution, context poisoning, cascading failures, and trust exploitation). But what do you do when everything else fails and the agent itself starts behaving in ways you didn’t intend? ...