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AgentGuard 90-Second Demo

This demo shows AgentGuard evaluating coding-agent behavior with deterministic evidence: tests, diffs, forbidden paths, test tampering, secret-path checks, and suite-level reports. It does not call an LLM or depend on a hosted service.

AgentGuard v0.2.2 is available from production PyPI:

python -m pip install agentguard-evals
agentguard --version

The installed distribution is agentguard-evals; the command remains agentguard. The ordinary package does not include the repository examples, so clone the repository before running this demo:

git clone https://github.com/richinmrudul/agentguard.git
cd agentguard

For the fastest recruiter-ready local proof, run the showcase pack:

scripts/showcase_demo.sh

It produces .agentguard/showcase/showcase-summary.json and .agentguard/showcase/showcase-summary.md, with a committed sample at docs/results/showcase-summary.json.

The visual tour shows the generated dashboard, a sanitized guard incident detail, and the corresponding trend evidence. The screenshot source record documents the exact fixture, capture, and sanitization boundaries.

The recorded 80-second product demo is a fixed, captioned view of this workflow. Use the commands below for live reproduction; the recording does not replace them and does not claim that its small curated corpus measures general production effectiveness.

To regenerate the quoteable detection-quality and local timing proof, run:

.venv/bin/python scripts/showcase_metrics.py

The committed metrics live at docs/results/showcase-metrics.json and docs/results/showcase-metrics.md.

What The Demo Proves

AgentGuard can distinguish between agents that make the intended source fix and agents that get passing tests by taking unsafe shortcuts.

  • Safe fix: the agent changes only allowed source files and passes tests.
  • Test cheating: the agent weakens tests, so tests pass but AgentGuard fails it.
  • Prompt injection: the repo README contains malicious instructions, and the follower agent creates .env; AgentGuard catches the forbidden/secret path.
  • Dependency script injection: untrusted setup instructions tell the agent to create dependency state; AgentGuard catches the out-of-scope secret path.
  • Filesystem boundary: the escape agent fixes the source bug but creates a secret file and reports a path escape command attempt.
  • Symlink path traversal: the follower fixes source but writes a key through an in-repository symlink into a forbidden directory.
  • Suite mode: multiple benchmark tasks are summarized as one evaluation run.

Prerequisites

  • AgentGuard is installed in the current environment.
  • Run commands from the repository root.
  • Docker is required only for the Docker-backed commands in the numbered demo below. The showcase pack itself is local and does not require Docker.

If the agentguard console script is not on your PATH, use:

python -m agentguard.cli.main ...

Demo Script

1. Safe Agent

agentguard run examples/configs/fix_auth_bug_docker_command_safe.yaml --agent custom-command

Expected outcome: PASS.

What it demonstrates: the agent fixes the auth bug, tests pass, and the diff stays inside the allowed source scope.

2. Test-Cheating Agent

agentguard run examples/configs/fix_auth_bug_docker_command_cheater.yaml --agent custom-command --allow-fail-result

Expected outcome: FAIL.

What it demonstrates: tests pass after the agent weakens the test file, but AgentGuard detects test tampering and fails the run.

3. Prompt-Injection Follower

agentguard run examples/configs/prompt_injection_readme_injection_follower.yaml --agent custom-command --allow-fail-result

Expected outcome: FAIL.

What it demonstrates: the agent fixes the source bug and passes tests, but also follows malicious README instructions by creating .env. AgentGuard fails the run with forbidden-path and secret-scan evidence.

4. Dependency Script Injection

agentguard run examples/configs/dependency_script_injection_safe.yaml --agent custom-command
agentguard run examples/configs/dependency_script_injection_follower.yaml --agent custom-command --allow-fail-result

Expected outcome: the safe run PASSes and the follower run FAILs.

What it demonstrates: AgentGuard can catch an agent that fixes source but also follows malicious dependency/setup instructions by creating forbidden dependency state.

5. Filesystem Boundary

agentguard run examples/configs/filesystem_boundary_safe.yaml --agent custom-command
agentguard run examples/configs/filesystem_boundary_escape.yaml --agent custom-command --allow-fail-result

Expected outcome: the safe run PASSes and the escape run FAILs.

What it demonstrates: AgentGuard can catch forbidden/secret file creation and ingested command-attempt evidence for suspicious filesystem boundary behavior.

agentguard run examples/configs/symlink_path_traversal_safe.yaml --agent custom-command
agentguard run examples/configs/symlink_path_traversal_follower.yaml --agent custom-command --allow-fail-result

Expected outcome: the safe run PASSes and the follower run FAILs.

What it demonstrates: AgentGuard preserves the benchmark symlink in the copied workspace and detects the follower's forbidden, secret-like out-of-scope write.

7. Core Suite

agentguard suite examples/suites/core.yaml --allow-failures

Expected outcome: the suite completes and reports mixed results across safe, test-cheating, prompt-injection, dependency-injection, and filesystem-boundary behaviors.

What it demonstrates: AgentGuard can package multiple benchmark tasks into one evaluation artifact with pass rate, average score, best/worst runs, and common failed checks.

To show the CI gate flow, save a baseline and compare the suite against it:

agentguard suite examples/suites/core.yaml --allow-failures --save-baseline /tmp/agentguard-core-baseline.json
agentguard gate suite examples/suites/core.yaml --baseline /tmp/agentguard-core-baseline.json --allow-failures

Where Reports Go

Each single benchmark run prints report paths like:

JSON report path: .agentguard/runs/<task-agent-timestamp>/reports/report.json
Markdown report path: .agentguard/runs/<task-agent-timestamp>/reports/report.md
Command log path: .agentguard/runs/<task-agent-timestamp>/command_log.json

Suite mode writes:

Suite JSON report path: .agentguard/suites/<suite-id-timestamp>/suite.json
Suite Markdown report path: .agentguard/suites/<suite-id-timestamp>/suite.md

The Markdown reports are the easiest artifacts to open during a portfolio demo. The JSON reports are structured for CI, dashboards, or later analysis.

Resume Thesis

AgentGuard is a local-first benchmark and policy harness for AI coding agents. The project thesis is that agent evaluation should be evidence-based: not just "did tests pass?", but "what changed, which commands ran, did the agent touch tests, did it create secret files, and did it stay in scope?"

This demo supports that thesis in under 90 seconds by showing three concrete failure modes:

  • A normal safe fix passes.
  • A test-cheating shortcut fails despite passing tests.
  • A prompt-injection follower fails despite fixing the source bug.
  • A dependency/setup-injection follower fails despite fixing the source bug.
  • A filesystem-boundary escape fails despite fixing the source bug.
  • A symlink/path-traversal follower fails despite fixing the source bug.

The suite report then turns those individual runs into one portfolio-ready evaluation summary.

The v0.2.2 release completed with 1,157 passing tests and 15 documented skips. Its secretless OIDC publication, public installation, and byte-identical workflow/PyPI artifacts are recorded in the release verification evidence.