Agent Accountability & Continuous Evaluation Toolkit

An evidence-based system to decide whether an agentic AI should be trusted in production.

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Why this project exists

Agentic AI systems are rapidly moving from pilots to production. However, most organizations still lack a clear way to answer a fundamental question:

“Should we trust this agent in our organization?”

This project was built to explore how enterprises can operationalize:

• Accountability
• Continuous evaluation
• Human-in-the-loop governance

Instead of focusing on model accuracy, this toolkit treats agents as actors, and trust as something that must be proven with evidence, not declared.

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Core principles

This project is guided by three principles that emerged repeatedly in enterprise discussions (e.g. RAISE 2025):

1. Agents are actors, not tools Agentic AI initiates actions and influences decisions. Traditional software testing is not sufficient.

2. Trust is evidence, not intention Trust requires:

   • Observability (what happened)
   • Traceability (why it happened)
   • Auditability (can it be reconstructed later)

3. Evaluation must be continuous Agent behavior can drift over time. Evaluation should not stop at deployment.

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What this toolkit does

At a high level, the system:

1. Runs an agent against risk-driven scenarios
2. Logs every agent action as immutable audit evidence
3. Evaluates behavior using rule-based + LLM-assisted metrics
4. Produces a deployment readiness signal (GO / CONDITIONAL / NO-GO)

This mirrors how real enterprises assess risk, not how demos are built.

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Project structure

The folder structure is intentional. Schemas and evidence come first; code comes second.

agent-accountability-eval/
├─ README.md                  # Project overview, problem framing, and demo entry point
├─ requirements.txt           # Python dependencies for reproducible runs
├─ .gitignore                 # Prevents committing logs, secrets, and local artifacts

├─ schemas/                   # Core evidence schemas (audit & evaluation)
│  ├─ run_record.schema.json  # Schema for agent run audit logs (observability & traceability)
│  └─ eval_result.schema.json # Schema for evaluation results and decision gates

├─ docs/
│  └─ threat_model.md         # Threat model describing key agent failure modes

├─ data/
│  ├─ scenarios/              # Scenario-based behavioral test cases
│  │  ├─ banking/             # Banking and financial risk scenarios
│  │  ├─ shadow_ai/           # Shadow AI and policy bypass scenarios
│  │  ├─ privacy/             # Privacy and data leakage scenarios
│  │  ├─ tool_abuse/          # Tool misuse and privilege escalation scenarios
│  │  └─ hallucination/       # Hallucination and overconfidence traps
│  └─ logs/                   # Append-only audit logs generated from agent runs

├─ src/
│  ├─ agent/
│  │  └─ runner.py            # Executes the agent under defined constraints and scenarios
│  ├─ audit/
│  │  └─ logger.py            # Captures and stores audit logs for every agent action
│  ├─ eval/
│  │  └─ metrics.py           # Behavior-based evaluation metrics and gate logic
│  └─ report/
│     └─ generator.py         # Generates agent readiness and decision reports

└─ demo/
   └─ run_demo.py             # End-to-end demo: scenario → agent → audit → evaluation → report

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Audit logging (Accountability)

Every agent run is recorded as an append-only JSONL audit log.

Each log captures:

• Inputs and context
• Constraints and permissions
• Tool usage
• Outputs and confidence
• Safety flags (policy, PII, hallucination risk)
• Human override signals
• Environment metadata for reproducibility

These logs are treated as evidence, not debug output.

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Evaluation & decision gates

Evaluation focuses on behavior, not accuracy.

Typical metrics include:

• Policy compliance
• Tool overreach
• Consistency across repeated runs
• Hallucination risk
• Behavior drift over time

Results are summarized using decision gates:

• GO — Safe to deploy
• CONDITIONAL — Deploy with restrictions / monitoring
• NO-GO — Do not deploy

This framing aligns with how enterprises actually make deployment decisions.

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Threat-driven scenarios

Scenarios are derived from an explicit threat model, not ad-hoc prompts.

Examples of covered risks:

• Unauthorized financial advice
• PII leakage
• Shadow AI policy bypass
• Tool privilege escalation
• Confident hallucinations

Each scenario exists to answer:

“What is the worst realistic failure mode here?”

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Quick start (demo)

Run a minimal end-to-end example that generates an audit log:

python demo/run_demo.py

You should see:

• A generated run_id
• A new audit log file in data/logs/
• A JSONL record representing one agent run

This demo intentionally prioritizes clarity over sophistication.

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Intended audience

This project is designed for:

• AI evaluation & quality engineers
• Responsible AI / AI governance practitioners
• Enterprise architects assessing agentic AI readiness
• Researchers exploring human-in-the-loop systems

It is not intended as a production agent framework.

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Status & roadmap

Current focus:

• Solid audit logging (done)
• Scenario-driven evaluation
• Clear decision outputs

Planned next steps:

• Scenario regression testing
• Drift detection over time
• Automated Agent Readiness Reports (1-page)

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Final note

As agentic AI becomes more autonomous, the most important question is no longer “How smart is the model?” but:

“Is the organization ready to take responsibility for this agent?”

This toolkit is a small step toward answering that question with evidence.