GitHub - muhammadwaqasai/agent_acid: ACID-style rollback and session-memory guardrails for autonomous AI agents · GitHub
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agent_acid
ACID-style transaction guarantees for autonomous AI agents.
AI agents that take real-world actions (charging cards, writing to databases, sending emails, calling APIs) have no standard way to "undo" a partially-completed task, and most guardrail systems only validate one action at a time — with no memory of the session. agent_acid closes both gaps:
Automatic rollback — if any step in a multi-step agent plan fails, every completed step before it is automatically undone, in reverse order.
Guardrails — hard, code-level rules (not prompts) that block bad AI outputs even when nothing "crashes."
Stateful (session-wide) guardrails — catch multi-step manipulation, like an attacker or a manipulated AI splitting one large forbidden action into several small, individually-legal ones ("salami slicing").
This isn't a theoretical framework — every claim below is backed by a runnable test or live demo in this repo.
Why this exists
Salami-slicing–style attacks against AI agents are an actively studied problem: guardrails that only judge one tool call at a time are "memoryless," letting an attacker spread a forbidden action across many small steps where no single step trips the alarm. agent_acid's stateful guardrails are built specifically to close this gap.
Quick start
pip install -r requirements.txt # or just: pip install openai pytest
Run the automated test suite (no API key needed, proves the core engine works):
pytest tests/test_core.py -v
Run the basic rollback demo (no API key needed):
python examples/basic_rollback.py
Run the guardrail demo (no API key needed):
python examples/guardrail_demo.py
Run the real AI-agent demos (requires an OpenAI API key):
export OPENAI_API_KEY="sk-..." # Mac/Linux<br>$env:OPENAI_API_KEY = "sk-..." # Windows PowerShell
python examples/llm_agent_demo.py<br>python examples/attack_test_prompt_injection.py<br>python examples/attack_test_salami_slicing.py<br>python examples/comparison_naive_agent.py
The core idea
blocked & fully refunded<br>])">from agent_acid.core import ReversibleTool, TransactionContext, AgentTransactionEngine<br>from agent_acid.guardrails import max_value, cumulative_max
charge_tool = ReversibleTool(<br>name="charge_card",<br>description="Charges a customer's card",<br>execute=lambda kwargs: real_payment_api.charge(kwargs),<br>compensate=lambda kwargs, result: real_payment_api.refund(result["charge_id"]),<br>guardrails=[max_value("amount", limit=500)],<br>stateful_guardrails=[cumulative_max("charge_card", "amount", session_limit=1000)],
engine = AgentTransactionEngine()<br>ctx = TransactionContext()<br>engine.execute_plan(ctx, [<br>(charge_tool, {"user_id": "u1", "amount": 400}),<br>(charge_tool, {"user_id": "u1", "amount": 400}),<br>(charge_tool, {"user_id": "u1", "amount": 400}), # total now 1200 -> blocked & fully refunded<br>])
Proven results
Attack 1: Prompt injection (hidden instruction overriding a spending limit)
A "customer ticket" contains a fake administrator note trying to convince the AI to charge $75,000 instead of following the $500 limit. The AI gets fooled and attempts the charge — agent_acid's guardrail blocks it and fully reverses the account creation and the charge.
Attack 2: Salami slicing (splitting one large charge into several small ones)
The AI is instructed to charge $1,200 as three separate $400 charges specifically to stay under a $500 per-step limit. Each individual charge passes the per-step check. agent_acid's cumulative guardrail tracks the running total across the whole session and blocks the third charge once the total crosses $1,000 — then rolls back all three charges and the account.
Head-to-head comparison
The same salami-slicing attack was run against a naive agent using only a per-step check (representative of how most simple guardrail integrations work):
Naive agent<br>agent_acid
Per-step limit ($500)<br>Enforced<br>Enforced
3× $400 charge attack<br>All 3 succeeded — $1,200 charged<br>Blocked on 3rd call
Rollback available?<br>No — money is gone<br>Yes — fully refunded
Account left...