| --- |
| license: apache-2.0 |
| tags: |
| - agentenv |
| - rl-environment |
| - synthetic |
| - finance |
| agentenv: |
| default: tradeops-baseline |
| configs: |
| - config_name: tradeops-baseline_tasks |
| data_files: |
| - split: eval |
| path: tasks/tradeops-baseline.parquet |
| default: true |
| - config_name: tradeops-baseline_episodes |
| data_files: |
| - split: eval |
| path: episodes/tradeops-baseline.parquet |
| - config_name: tradeops-agent_tasks |
| data_files: |
| - split: eval |
| path: tasks/tradeops-agent.parquet |
| - config_name: tradeops-agent_episodes |
| data_files: |
| - split: eval |
| path: episodes/tradeops-agent.parquet |
| - config_name: tradeops-host-cli_tasks |
| data_files: |
| - split: eval |
| path: tasks/tradeops-host-cli.parquet |
| - config_name: tradeops-host-cli_episodes |
| data_files: |
| - split: eval |
| path: episodes/tradeops-host-cli.parquet |
| --- |
| |
| # TradeOpsSim |
|
|
| <video controls preload="metadata" width="100%" poster="https://huggingface.co/datasets/AlexSchwartzman/TradeOpsSim-AgentEnv/resolve/main/media/poster.png"> |
| <source src="https://huggingface.co/datasets/AlexSchwartzman/TradeOpsSim-AgentEnv/resolve/main/media/tradeops-demo.mp4" type="video/mp4"> |
| <track kind="captions" src="https://huggingface.co/datasets/AlexSchwartzman/TradeOpsSim-AgentEnv/resolve/main/media/captions.vtt" srclang="en" label="English"> |
| </video> |
|
|
| AgentEnv is a framework for building software environments and evaluating agents in them. |
| TradeOpsSim is one example: engineers supply a trading workflow, its tools and business checks; |
| the framework deploys the components, runs the cases and retains the results. |
|
|
| An original synthetic trading-operations environment: reconcile a delayed execution, a repeated |
| delivery, and an interrupted cancellation without inventing missing evidence. Thirteen fixed |
| cases cover clean records, partial recovery, account scope, rejected cancellations, completed |
| orders, sell-side signs, and incomplete sources. No market data or production system is connected. |
|
|
| The rules baseline and AI agents use the same nine tools. The evaluator checks immutable source |
| evidence separately. A correctly escalated unresolved case can pass; a green task status alone |
| does not establish success. Final model tasks also require a completed, error-free agent response. |
|
|
| All native runs in the baseline and host export store are retained, including development attempts |
| and infrastructure failures. Separate API contract tests and the video's UI runs are documented |
| in the validation notes and video source package. |
| The task_version field distinguishes earlier task graphs from the final combined reward graph. |
| Runs that fail before a model prompt have no model trajectory and are excluded from model-quality |
| denominators. These thirteen authored fixtures are a demonstration, not a held-out benchmark or |
| evidence of production performance. |
| |
| The portable baseline and API-agent bundles include their complete Docker build contexts. |
| The host-CLI bundle records the actual model trials and requires the source runner. |
| |
| ## Watch and try it |
| |
| [Watch the video and inspect recorded runs](https://huggingface.co/spaces/AlexSchwartzman/TradeOpsSim). The Space shows existing synthetic results. Download the environment to run your own agent. |
| |
| [Watch the narrated AgentEnv walkthrough](https://huggingface.co/datasets/AlexSchwartzman/TradeOpsSim-AgentEnv/resolve/main/media/tradeops-demo.mp4). Suppose GTS wanted an agent |
| to monitor trade reconciliation, investigate mismatches, and flag cases for review. The film shows |
| how AgentEnv could help the team build a test environment and evaluate that agent. Two recorded |
| synthetic reconciliation runs illustrate the checks; the GTS pilot and internal trial are proposals. |
| The film combines actual application captures with explanatory diagrams, with |
| [captions](media/captions.vtt) and a [transcript](media/TRANSCRIPT.md). |
| |
| [Using AgentEnv internally](docs/agentenv-workflow.md) describes the records, tools and outcome |
| checks a team supplies, the test execution AgentEnv manages, and concrete reproduction commands. |
| |
|  |
| |
| Download the [application source](downloads/tradeops-sim-0.1.0.tar.gz) for the operator UI, |
| MCP server, all thirteen cases, tests, and AgentEnv publishing tools. The |
| [editable video package](downloads/tradeops-video-package.zip) contains the film's local assets, |
| original captures, run evidence, provenance, and pinned HyperFrames source. |
| |
| To run the baseline, download this repository and use Python 3.11.4+ with Docker running: |
| |
| ```bash |
| python -m pip install 'agentenv-framework==0.9.1324' |
| agent-env run ./bundles/tradeops-baseline --task late_fill_duplicate |
| ``` |
| |
| The optional HF plugin also supports downloading and running the bundle directly: |
| |
| ```bash |
| python -m pip install 'agentenv-hf @ git+https://github.com/earakely-scale/agentenv-hf-plugin@c116bf1b321fa33d671632b0ee1b77406dbe3b58' |
| agent-env hf run AlexSchwartzman/TradeOpsSim-AgentEnv --bundle tradeops-baseline --task late_fill_duplicate |
| ``` |
| |
| The default bundle is |
| `tradeops-baseline`; it requires no model key. Use `tradeops-agent` for a configured API model; |
| its bundle README specifies runtime configuration. Reproduce the host CLI trials from the |
| source archive, not with the standalone HF run command. |
| |
| ## Recorded validation |
| |
| | Track | Published task rows | Published episode rows | Actual model trajectories | |
| | --- | ---: | ---: | ---: | |
| | Rules baseline | 13 | 14 | 0 | |
| | Portable API agent | 13 | 0 | 0 | |
| | Host CLI model | 13 | 28 | 27 | |
| |
| The final task-version-2 model sweep passed all thirteen authored cases: nine reconciliations and |
| four correct unresolved outcomes. The configured model identifier was `claude-sonnet-5-5`. |
| The baseline passed the same thirteen cases. The baseline and host tracks each retain one earlier |
| pre-prompt infrastructure failure with a null reward. The API adapter passed a full Docker/Task.run contract |
| test using a scripted provider; that test is not an AI result. See [validation](docs/validation.md) |
| for scope, negative controls, and reproduction commands. |
| |
| Six Parquet configurations separate tasks from episodes for each track. `raw/` preserves native |
| run records and supported trajectories; `bundles/` contains frozen executable artifacts. |
| Task versions distinguish the early business-only score from the final score that also requires |
| successful agent completion. A null reward denotes an ungraded infrastructure failure. |
| |
| <!-- agentenv-hf:tradeops-baseline --> |
| Transparent rules policy. No model or API key is required. This is a software baseline, not an AI result. |
| <!-- /agentenv-hf:tradeops-baseline --> |
| |
| <!-- agentenv-hf:tradeops-agent --> |
| Portable A2A model bundle. Supply a compatible API endpoint, credential, and model at runtime. Its API contract is tested locally; the recorded real-model trials use the separately identified host CLI adapter. |
| <!-- /agentenv-hf:tradeops-agent --> |
| |
| <!-- agentenv-hf:tradeops-host-cli --> |
| Supplemental recorded host CLI trials. Reproduce with scripts/run_host_trials.py from the source distribution and an authenticated host CLI. This task bundle is not a standalone agent-env hf run target. |
| <!-- /agentenv-hf:tradeops-host-cli --> |
| |
