safeagent-exec-guard 0.1.5

Deterministic execution guard for AI agents: request-id dedup + finality gating + durable state.

SafeAgent

Deterministic execution guard for AI agents.

pip install safeagent-exec-guard

Prevents duplicate, replayed, or premature irreversible actions triggered by LLM-based agents by enforcing:

• request-id (nonce) deduplication
• confidence / consensus thresholds before finality
• deterministic state transitions
• exactly-once settlement/execution semantics
• durable state persistence (SQLite)

This repository is a reference implementation and pattern demo (not a hosted service).

---

Install

---

Exactly-once tool execution

from settlement.settlement_requests import SettlementRequestRegistry

registry = SettlementRequestRegistry()

def send_email():
    print("SENT EMAIL")

receipt = registry.execute(
    request_id="email:C123:invoice",
    action="send_email",
    payload={"to": "[e-mail]", "template": "invoice"},
    execute_fn=send_email,
)

print(receipt)

If the same request_id is replayed, SafeAgent returns the original receipt instead of executing the side effect again.

pip install safeagent-exec-guard

Requires Python 3.10+.

---

Why SafeAgent?

LLM agents frequently retry tool calls or replay events when something fails.

Without a guard layer, this can cause duplicate execution of irreversible actions (tickets, emails, payouts, trades).

Without SafeAgent

create_support_ticket(customer_id="C123", severity="high")
create_support_ticket(customer_id="C123", severity="high")  # duplicate

With SafeAgent (exactly-once execution)

from settlement.settlement_requests import SettlementRequestRegistry

registry = SettlementRequestRegistry()

receipt = registry.execute(
    request_id="agent_action_123",
    action="create_support_ticket",
    payload={"customer_id": "C123", "severity": "high"}
)

print(receipt)

If the agent retries the same request_id, SafeAgent returns the original receipt instead of executing again.

---

Quick Example

from settlement.models import Case
from settlement.store import SQLiteStore

store = SQLiteStore("safeagent.db")
case = Case(case_id="example_case")
store.put_case(case)

print("SafeAgent initialized:", case.state)

---

What problem does this solve?

AI agents running in production often:

• retry tool calls on partial failure
• replay webhook events
• execute the same action twice
• act on provisional/ambiguous outcomes
• loop under uncertainty and produce conflicting signals

When agents touch real systems (tickets, emails, DB writes, payouts, trades), duplicate execution becomes expensive.

SafeAgent provides a control-plane pattern that sits between an agent decision and an irreversible action and only allows execution when the outcome is deterministically FINAL.

---

High-level flow

Agent / Outcome Signals
→ Reconciliation / Consensus
→ Finality Gate
→ Execution (exactly-once)
→ Audit / Receipt

---

State machine

OPEN
→ RESOLVED_PROVISIONAL
→ IN_RECONCILIATION
→ FINAL
→ SETTLED

• Ambiguous signals transition to IN_RECONCILIATION
• Execution is impossible unless state is FINAL
• Execution is replay-safe (idempotent)
• Late signals are ignored after finality

---

Key features

Durable persistence (SQLiteStore)

• Case state and signals can be persisted to SQLite
• State survives restarts
• ACID durability for single-node safety

Request-id (nonce) deduplication

• Settlement/execution attempts require a unique request_id
• Replays using the same request_id return the cached result
• New request_ids after settlement resolve to the same settlement_id
• Prevents duplicate effects across retries or multiple actors

Confidence / consensus threshold policy

• Auto-finalize outcomes when agreement exceeds a threshold (e.g. 80%)
• Falls back to majority decision when threshold is not met (in demo)

---

Demos

1) SafeAgent demo (duplicate execution prevention)

python examples/safe_agent_demo.py

2) AI outcome simulation (stochastic agent signals)

python examples/simulate_ai.py

3) Persistence demo (prove restart safety)

Run twice:

python examples/persist_demo.py
python examples/persist_demo.py

---

Core implementation structure

models.py                      case + signal models
state_machine.py               deterministic transitions
reconciliation.py              conflict detection & resolution
gate.py                        settlement/execution gate
store.py                       in-memory + SQLite persistence

policy.py                      confidence/threshold decision helper
settlement_requests.py         request-id (nonce) dedup wrapper

examples/safe_agent_demo.py            SafeAgent demo
examples/simulate_ai.py                AI demo
examples/persist_demo.py               persistence demo
examples/nonce_demo.py                 nonce demo

---

Need this in production?

If you’re implementing this pattern in a production agent system and want help tailoring reconciliation rules, execution receipts, or persistence/concurrency strategy, see LICENSING.md.

---

License

Apache-2.0