| # AgentRx 🩺 |
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| **Diagnosing AI Agent Failures from Execution Trajectories** |
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| [[Paper]](https://arxiv.org/abs/2602.02475) [[Dataset]](https://huggingface.co/datasets/microsoft/AgentRx) |
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| AI agents often fail in ways that are difficult to localize — executions are probabilistic, long-horizon, multi-agent, and mediated by noisy tool outputs. **AgentRx** is an automated, domain-agnostic diagnostic framework that pinpoints the *critical failure step* in a failed agent trajectory. It synthesizes constraints (invariants), evaluates them step-by-step, and produces an auditable validation log of constraint violations with associated evidence. An LLM-based judge uses this log to localize the critical step and classify the failure into a grounded 10-category taxonomy. |
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| AgentRx improves step localization and failure attribution over existing baselines across three domains: structured API workflows (Tau-bench), incident management (Flash), and open-ended web/file tasks (Magentic-One). |
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|
| ``` |
| Raw logs ──▶ Trajectory IR ──▶ Invariants ──▶ Checker ──▶ Judge ──▶ Reports |
| ``` |
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| --- |
|
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| ## Quick Start |
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| ```bash |
| # Setup |
| python -m venv .venv |
| .venv/Scripts/activate # Windows; use `source .venv/bin/activate` on Linux/Mac |
| pip install -e . # installs agentrx + all dependencies |
| cp .env.example .env # Fill in your Azure or TRAPI endpoint details |
| |
| # Local dev: skip ManagedIdentity IMDS probe |
| export AZURE_TOKEN_CREDENTIALS=dev # or add to your .env file |
| |
| # Run the full pipeline end-to-end |
| python run.py trajectory.json |
| |
| # Specify domain explicitly |
| python run.py trajectory.json --domain tau |
| ``` |
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| You can also install directly from GitHub without cloning: |
| ```bash |
| pip install git+https://github.com/microsoft/AgentRx.git |
| ``` |
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| All outputs are saved to `runs/<run_name>/`. |
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| --- |
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| ## Step-by-Step Usage |
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| You can run each stage individually and inspect the results between stages: |
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| ```bash |
| # 1. Normalize raw logs into Trajectory IR |
| python run.py trajectory.json --stage ir --run-name my_run |
| |
| # 2. Generate static invariants |
| python run.py trajectory.json --stage static --run-dir runs/my_run |
| |
| # 3. Generate dynamic (per-step) invariants |
| python run.py trajectory.json --stage dynamic --run-dir runs/my_run |
| |
| # 4. Check all invariants against the trajectory |
| python run.py trajectory.json --stage check --run-dir runs/my_run |
| |
| # 5. Run LLM judge for root-cause classification |
| python run.py trajectory.json --stage judge --run-dir runs/my_run |
| |
| # 6. Generate report plots |
| python run.py trajectory.json --stage report --run-dir runs/my_run |
| ``` |
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| --- |
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| ## Pipeline Stages |
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| | # | Stage | Output | |
| |---|-------|--------| |
| | 1 | **IR** — Normalize raw logs into canonical Trajectory IR | `trajectory_ir.json` | |
| | 2 | **Static** — Generate policy/tool/structure invariants | `static_invariants.json` | |
| | 3 | **Dynamic** — Generate per-step context-aware invariants | `dynamic_invariants/` | |
| | 4 | **Check** — Evaluate invariants, record violations | `checker_results/` | |
| | 5 | **Judge** — LLM classifies root-cause failure (10-category taxonomy) | `judge_output/` | |
| | 6 | **Report** — Failure frequency plots | `plots/` | |
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| --- |
|
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| ## Directory Structure |
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| ``` |
| AgentRx/ |
| ├── run.py # CLI entry point (backward-compatible) |
| ├── pyproject.toml # Package configuration (pip install -e .) |
| ├── requirements.txt # Python dependencies |
| ├── agentrx/ # Main package |
| │ ├── cli.py # Console script entry point |
| │ ├── ir/ # Trajectory IR normalization |
| │ ├── invariants/ # Invariant generation & checking |
| │ ├── judge/ # LLM-as-a-Judge evaluation |
| │ ├── llm_clients/ # Azure OpenAI & TRAPI clients |
| │ ├── pipeline/ # Config (globals.py), utilities |
| │ └── reports/ # Analysis & visualization |
| ├── data/ # Domain policies, tool schemas, ground truth |
| ├── trajectories/ # Sample trajectories (tau, magentic, test) |
| └── runs/ # Pipeline outputs (one folder per run) |
| ``` |
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| --- |
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| ## Supported Domains |
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| | Domain | Flag | Description | |
| |--------|------|-------------| |
| | **tau** | `--domain tau` | Tau-bench retail customer service | |
| | **magentic** | `--domain magentic` | Magentic-One multi-agent | |
| | **flash** | `--domain flash` | Flash/orchestrator incident traces | |
| | *(auto)* | *(default)* | Auto-detected; unknown formats use LLM-based IR fallback | |
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| --- |
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| ## Configuration |
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| LLM settings are loaded from environment variables (via `.env` or shell): |
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| Copy the template and fill in your values: |
| ```bash |
| cp .env.example .env |
| ``` |
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| ```bash |
| # Azure OpenAI (default endpoint) |
| AGENT_VERIFY_ENDPOINT= # e.g., "https://my-resource.openai.azure.com/" |
| AGENT_VERIFY_DEPLOYMENT= # e.g., "gpt-5" |
| AGENT_VERIFY_MODEL_NAME= # e.g., "gpt-5" (must match deployment; used by judge) |
| |
| # TRAPI (Microsoft Research internal, use --endpoint trapi) |
| AGENT_VERIFY_TRAPI_INSTANCE= # e.g., "my-instance/my-pool" |
| AGENT_VERIFY_TRAPI_DEPLOYMENT_NAME= # e.g., "my-deployment-name" |
| SCOPE= # Azure AD scope for TRAPI |
| ``` |
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| Both endpoints use **Azure AD token-based auth** (`az login` or Managed Identity). |
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| > **Note:** TRAPI is a Microsoft Research internal endpoint. External teams should use `--endpoint azure` (default). |
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| --- |
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| ## Failure Taxonomy |
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| | # | Category | Description | |
| |---|----------|-------------| |
| | 1 | **Instruction/Plan Adherence Failure** | Skips steps or adds unnecessary actions | |
| | 2 | **Invention of New Information** | Fabricates or omits ungrounded facts | |
| | 3 | **Invalid Invocation** | Malformed tool call (wrong args/types/schema) | |
| | 4 | **Misinterpretation of Tool Output** | Incorrect reasoning about tool results | |
| | 5 | **Intent-Plan Misalignment** | Pursues wrong objective | |
| | 6 | **Underspecified User Intent** | Missing information to proceed | |
| | 7 | **Intent Not Supported** | Action can't be performed with available tools | |
| | 8 | **Guardrails Triggered** | Blocked by safety/RAI/access policies | |
| | 9 | **System Failure** | Infra errors (timeouts, unreachable endpoints) | |
| | 10 | **Inconclusive** | Insufficient evidence to classify | |
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| --- |
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| ## Running Individual Modules |
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| Each module can also be run standalone: |
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| **Static Invariant Generator** — generate policy/tool invariants: |
| ```bash |
| python agentrx/invariants/static_invariant_generator.py --input-path trajectory.json --domain tau |
| ``` |
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| **Dynamic Invariant Generator** — generate per-step context-aware invariants: |
| ```bash |
| python agentrx/invariants/dynamic_invariant_generator.py --input-path trajectory.json --domain tau --mode stepbystep |
| ``` |
|
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| **Checker** — evaluate invariants against a trajectory: |
| ```bash |
| python agentrx/invariants/checker.py --input-path trajectory.json --static-invariants static_inv.json --dynamic-invariants-dir dyn_inv/ |
| ``` |
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| **Judge** — run LLM-as-a-Judge classification: |
| ```bash |
| python agentrx/judge/judge.py --domain tau --log_file trajectory.json --mode combined |
| ``` |
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| --- |
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| ## Third-Party Code |
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| This project uses the following third-party open source packages (installed via `requirements.txt`): |
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| - **openai** — OpenAI Python client (MIT License) |
| - **azure-identity** / **azure-core** — Azure SDK authentication (MIT License) |
| - **matplotlib** — Plotting and visualization (PSF-based License) |
| - **tiktoken** — Token counting (MIT License) |
| - **httpx** — HTTP client (BSD License) |
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| See [requirements.txt](requirements.txt) for the full list of dependencies. |
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| --- |
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| ## Troubleshooting |
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| ### `DefaultAzureCredential` timeout on local machines |
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| The Azure SDK's `DefaultAzureCredential` tries `ManagedIdentityCredential` before `AzureCliCredential`. On a local dev machine this probes the IMDS endpoint which doesn't exist locally, causing a ~5-10s timeout before falling back. This is expected behavior — the probe is how `DefaultAzureCredential` detects the hosting environment. |
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| **Fix:** Set the `AZURE_TOKEN_CREDENTIALS` environment variable to `dev` to exclude deployed-service credentials (e.g. `ManagedIdentityCredential`, `WorkloadIdentityCredential`) from the chain, so `DefaultAzureCredential` skips straight to developer-tool credentials like `AzureCliCredential`: |
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| ```bash |
| # PowerShell |
| $env:AZURE_TOKEN_CREDENTIALS = "dev" |
| |
| # Bash / Linux / macOS |
| export AZURE_TOKEN_CREDENTIALS=dev |
| ``` |
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| Or add `AZURE_TOKEN_CREDENTIALS=dev` to your `.env` file. |
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| > Requires `azure-identity >= 1.23.0`. See [Exclude a credential type category](https://learn.microsoft.com/azure/developer/python/sdk/authentication/credential-chains?tabs=dac#exclude-a-credential-type-category) for details. |
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| --- |
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| ## Contributing |
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| This project welcomes contributions and suggestions. Most contributions require you to agree to a |
| Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us |
| the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. |
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| Please see [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines. |
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| --- |
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| ## Trademarks |
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| This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft |
| trademarks or logos is subject to and must follow |
| [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). |
| Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. |
| Any use of third-party trademarks or logos are subject to those third-party's policies. |
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| --- |
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| ## License |
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| Copyright (c) Microsoft Corporation. All rights reserved. |
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| Licensed under the [MIT](LICENSE.txt) license. |
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| --- |
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| ## Citation |
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| If you use AgentRx, please cite: |
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| ```bibtex |
| @article{barke2026agentrx, |
| title={AgentRx: Diagnosing AI Agent Failures from Execution Trajectories}, |
| author={Barke, Shraddha and Goyal, Arnav and Khare, Alind and Singh, Avaljot and Nath, Suman and Bansal, Chetan}, |
| journal={arXiv preprint arXiv:2602.02475}, |
| year={2026} |
| } |
| ``` |
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