Category: Observability

When Prompts Feel Like Programming Blindfolded

After more than a year, on and off, building agents across LangFlow, Microsoft Agent Framework, and Copilot Studio – from PoCs to my own real-world deployments – one theme keeps nagging at me: prompt debugging feels like a black box adventure.

In traditional software development, you can step through the code, trace errors, and monitor state changes with powerful tools. But with natural language programming? You’re trusting your instructions to a probabilistic model whose reasoning you rarely get to see.

And that changes everything.

Reading LLMs Like Patients: What DSM-5 Can Teach Us About AI Behaviour

Most of the time, when we talk about large language models (LLMs), we end up in the weeds of training data and parameter counts. Useful if you’re a researcher; less useful if you’re a leader, policymaker, or practitioner trying to answer a simpler question:

“Is this thing actually behaving in a way I’m comfortable with?”

Two realities make that hard:

The training data is too large for humans to grasp in any meaningful way.

The models are too complex for us to truly understand their internal “decision making.”

But their outputs – the words they put on the page – are something we can read, interrogate, and assess.

Mitigating Risks in LLMs: How Observability Enhances AI Reliability

Agentic AI and LLM tools enable remarkable capabilities, from automating workflows to generating content and insights. As I spend more time in Langflow and really start to appreciate the power of the systems that can be developed I started wondering about how they could be monitored, how do we implement Observability – then a note about Datadog LLM Observability came across my feed and got me thinking that this is worth looking at more deeply.