agentrx 0.1.0

AgentRx – Agentic trajectory analysis pipeline for invariant checking and LLM-as-a-Judge evaluation

AgentRx 🩺

Diagnosing AI Agent Failures from Execution Trajectories

[Paper] [Dataset]

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.

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).

Raw logs ──▶ Trajectory IR ──▶ Invariants ──▶ Checker ──▶ Judge ──▶ Reports

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Quick Start

# 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

You can also install directly from GitHub without cloning:

pip install git+https://github.com/microsoft/AgentRx.git

All outputs are saved to runs/<run_name>/.

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Step-by-Step Usage

You can run each stage individually and inspect the results between stages:

# 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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Pipeline Stages

#	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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Directory Structure

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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Supported Domains

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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Configuration

LLM settings are loaded from environment variables (via .env or shell):

Copy the template and fill in your values:

cp .env.example .env

# Azure OpenAI (default endpoint)
AGENT_VERIFY_ENDPOINT=                # e.g., "https://my-resource.openai.azure.com/"
AGENT_VERIFY_DEPLOYMENT=              # e.g., "gpt-5"

# 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

Both endpoints use Azure AD token-based auth (az login or Managed Identity).

Note: TRAPI is a Microsoft Research internal endpoint. External teams should use --endpoint azure (default).

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Failure Taxonomy

#	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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Running Individual Modules

Each module can also be run standalone:

Static Invariant Generator — generate policy/tool invariants:

python agentrx/invariants/static_invariant_generator.py --input-path trajectory.json --domain tau

Dynamic Invariant Generator — generate per-step context-aware invariants:

python agentrx/invariants/dynamic_invariant_generator.py --input-path trajectory.json --domain tau --mode stepbystep

Checker — evaluate invariants against a trajectory:

python agentrx/invariants/checker.py --input-path trajectory.json --static-invariants static_inv.json --dynamic-invariants-dir dyn_inv/

Judge — run LLM-as-a-Judge classification:

python agentrx/judge/judge.py --domain tau --log_file trajectory.json --mode combined

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Third-Party Code

This project uses the following third-party open source packages (installed via requirements.txt):

• 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)

See requirements.txt for the full list of dependencies.

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Troubleshooting

DefaultAzureCredential timeout on local machines

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.

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:

# PowerShell
$env:AZURE_TOKEN_CREDENTIALS = "dev"

# Bash / Linux / macOS
export AZURE_TOKEN_CREDENTIALS=dev

Or add AZURE_TOKEN_CREDENTIALS=dev to your .env file.

Requires azure-identity >= 1.23.0. See Exclude a credential type category for details.

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Contributing

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.

Please see CONTRIBUTING.md for detailed guidelines.

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Trademarks

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. 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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License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.

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Citation

If you use AgentRx, please cite:

@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}
}