Cage-lite:Prebind assurance for consequential AI agent actions

Roopam W Sure released CAGE-lite v0.1.2, an open-source Python implementation of the CAGE (Control, Assurance, and Governance Evaluation) framework that provides prebind assurance for AI agent actions before they become binding business consequences. The tool, available on PyPI and GitHub, evaluates whether agent actions such as payments or access grants are authorized and supported by required evidence, recording decisions as CAGE Warrants. The release includes a Streamlit dashboard and replay demos for a standard USD 75,000 held-to-admitted payment scenario.

Prebind assurance for AI-agent actions at the business consequence boundary. Try the live CAGE-lite demo → Install from PyPI https://pypi.org/project/cage-lite/ · View release v0.1.2 https://github.com/roopamwsure/cage-lite/releases/tag/v0.1.2 Agent platforms govern how agents run. CAGE governs whether agent actions are allowed to become business consequences. CAGE-lite is my open-source reference implementation of CAGE: Control, Assurance, and Governance Evaluation . The broader framework is introduced in CAGE-1 https://arxiv.org/abs/2607.03510 . CAGE-lite focuses on making its Prebind Assurance model visible, testable, and easier to evaluate in practice. The project started with a simple question: An AI agent can propose an action, but what should happen before that action becomes real? Before an agent releases a payment, grants access, approves a transaction, updates a system of record, or discloses protected information, an organization should be able to verify that the action is authorized and supported by the required evidence. CAGE adds that final assurance step before the action becomes a binding business consequence. CAGE-lite is currently a v1 product preview . The Python package version is 0.1.2 , and the current CAGE Warrant schema is version 0.4 . CAGE-1 defines a broader evaluation framework across twelve areas. This release focuses on the Prebind Assurance path: evaluating an action before it crosses the business consequence boundary, recording the decision, and proving whether the resulting effect was blocked or allowed to bind. The CAGE-lite dashboard shows the latest boundary decision, the original held action, recent boundary runs, and the result of replaying the action after the missing evidence is supplied. The hosted demo automatically loads the standard USD 75,000 held-to-admitted replay, allowing visitors to inspect the boundary decision, CAGE Warrants, replay linkage, and effect proof without installing anything locally. CAGE-lite requires Python 3.10 or later. In an active Python 3.10 or later environment: python -m pip install cage-lite python -m cage lite.demo.payment replay This runs the packaged payment replay and writes the generated artifacts under playground/v04-replay-demo/ in the current working directory. To run the Streamlit dashboard, inspect the examples, or contribute changes, install CAGE-lite from source. git clone https://github.com/roopamwsure/cage-lite.git cd .\cage-lite python -m venv .venv .\.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip python -m pip install -e . python -m cage lite.demo.payment replay python -m streamlit run cage lite/ui/app.py git clone https://github.com/roopamwsure/cage-lite.git cd cage-lite python3 -m venv .venv source .venv/bin/activate python -m pip install --upgrade pip python -m pip install -e . python -m cage lite.demo.payment replay python -m streamlit run cage lite/ui/app.py The replay demo creates one original HELD Warrant and one linked ADMITTED replay Warrant under: playground/v04-replay-demo/ The Streamlit application loads those artifacts by default. Developer controls remain hidden unless they are explicitly enabled. The examples/ directory contains smaller demonstrations of individual CAGE behaviors: payment policy demo.py evaluates the payment policy and produces a held boundary decision without attempting an effect. payment no bind demo.py shows that a held action does not execute and records durable NO BIND effect proof. payment approval demo.py adds the required approval, admits the action, executes the protected effect, and records BOUND proof. payment narrowed demo.py narrows the requested payment to the agent's permitted scope and records the scoped effect result. Run an example from the repository root: python examples/payment no bind demo.py python examples/payment approval demo.py python examples/payment narrowed demo.py The examples write local CAGE Warrants, evidence records, and effect records under playground/ . The generated output is excluded from Git. CAGE does not replace agent runtimes, IAM, policy engines, guardrails, gateways, approval systems, observability platforms, or agent evaluation frameworks. Those systems produce important signals. CAGE consumes those signals and evaluates whether a proposed action should be allowed to cross the business consequence boundary. The diagram above shows the broader CAGE framework. CAGE-lite is the open-source implementation used to make this assurance model visible, testable, and easier to evaluate. A simplified CAGE flow looks like this: Agent proposes an action | v Identity, standing, policy, and approval signals | v CAGE prebind boundary | +---- HELD ---- NO BIND ---- Business effect blocked | +---- ADMITTED - BOUND ------ Business effect executed | v CAGE Warrant and effect proof Each evaluated action produces a CAGE Warrant containing decision proof, effect proof, evidence references, replay linkage, and integrity information. The Warrant distinguishes between deciding that an action may proceed and proving what happened after that decision. The included demo begins with a USD 75,000 vendor payment that exceeds the agent's USD 50,000 direct standing limit. Without the required human approval, CAGE holds the action before effect execution: - boundary: HELD ; - effect: NO BIND ; - system of record: NOT WRITTEN . The action is then replayed after approval evidence is added. The action, amount, standing limit, and policy remain unchanged. Only the approval state changes. The replay is admitted, the effect is allowed to bind, and the original held Warrant remains preserved and linked to the replay Warrant. Live CAGE-lite demo https://cage-lite.streamlit.app/ CAGE-lite on PyPI https://pypi.org/project/cage-lite/ CAGE-lite v0.1.2 release https://github.com/roopamwsure/cage-lite/releases/tag/v0.1.2 CAGE-1 on arXiv https://arxiv.org/abs/2607.03510 CAGE-1 author-hosted PDF https://roopamwsure.github.io/publications/cage-1/CAGE-1 Control Assurance Governance Evaluation.pdf Changelog /roopamwsure/cage-lite/blob/main/CHANGELOG.md Contributing guide /roopamwsure/cage-lite/blob/main/CONTRIBUTING.md Security policy /roopamwsure/cage-lite/blob/main/SECURITY.md Report an issue or request a feature https://github.com/roopamwsure/cage-lite/issues CAGE-lite is licensed under the Apache License 2.0 /roopamwsure/cage-lite/blob/main/LICENSE .