Add project blog post (OpenEnv Flow Debugger)

blog.md

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+# Debugging Power Automate Flows with OpenEnv
+
+Modern AI is changing. Instead of just predicting things, AI is now becoming an "agent" that can look at a problem, take action, and learn from the results. Most AI projects focus on games or robots, but in this project, I wanted to focus on a real-world problem: **fixing broken Power Automate flows.**
+
+I created a simple environment using **OpenEnv** to model a very common issue: **HTTP 400 BadRequest** errors caused by mistakes in "Condition" steps.
+
+## Why Power Automate?
+
+If you work with automation, you know that debugging is a slow and annoying task. Usually, it looks like this:
+
+1. A flow fails with a vague "400 BadRequest" error.
+2. The error is hidden inside a Condition step.
+3. There is a small typo in the formula (expression).
+4. An engineer has to find the mistake and fix it manually.
+
+This "debug-fix-check" loop is perfect for an AI agent. The agent sees the failure, tries a fix, and keeps going until the flow works.
+
+---
+
+## How the Environment Works
+
+I built this environment to be simple and clear. Each "episode" is basically one debugging session.
+
+### The Task
+
+The agent gets a failed flow run. The goal is simple: **Fix the broken expression so the flow succeeds.**
+
+### What the Agent Sees (Observations)
+
+The agent receives a JSON-like summary of the problem, including:
+
+* The error message and status.
+* Which step failed.
+* The inputs and outputs of the flow.
+* How many attempts are left.
+
+### What the Agent Can Do (Actions)
+
+To keep things simple for now, the agent has one job: **Send a patch.** It tells the system which step to change and provides the new, corrected expression. If the string matches the correct fix, the flow succeeds.
+
+### The Reward System
+
+I used a very simple scoring system:
+
+* **+1.0:** The flow is fixed!
+* **-0.1:** Wrong fix (but you can try again).
+* **-0.2:** Out of attempts (failed).
+
+---
+
+## The Data and the Demo
+
+* **The Dataset:** I created a set of JSON files with real-world bug examples. The agent has to figure out the fix based only on the error logs; it never sees the "correct answer" beforehand.
+* **The Demo Agent:** I built a simple agent that uses basic rules to find typos. It solved all the cases, which proves that the environment works and the feedback loop is solid.
+
+---
+
+## Keeping it Simple (Limitations)
+
+This is an MVP (Minimum Viable Product). To keep it fast and easy to use:
+
+* It only focuses on **Condition** expressions.
+* It doesn't actually connect to the real Power Automate website.
+* It is deterministic (no random surprises).
+
+---
+
+## Why This Matters
+
+Debugging is a huge part of real work, but it’s not often used in AI benchmarks. By turning Power Automate errors into an **OpenEnv** environment, I’m trying to bridge the gap between practical automation and AI research.
+
+In the future, I want to add more complex errors (like "Filter array" issues) and try training smarter agents using LLMs.
+
+**Conclusion:** AI agents need to learn how to handle messy, real-world systems. This project is a small step toward making AI more helpful in our daily office tasks.