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Agent Invariant Coverage Gap Resolver

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Find missing verification obligations across reachable agent execution paths. Structured invariants, supplied evidence and auditable gaps. No test execution.

Pricing

from $3.00 / 1,000 coverage gap assessments

Rating

0.0

(0)

Developer

Alessandro Bonometti

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Total users

Monthly active users

a day ago

Last modified

Categories

Agents

Developer tools

You can access the Agent Invariant Coverage Gap Resolver programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = { "request": {
9        "graph": {
10            "nodes": [
11                {
12                    "id": "planner",
13                    "type": "agent",
14                },
15                {
16                    "id": "worker",
17                    "type": "agent",
18                },
19                {
20                    "id": "payment",
21                    "type": "effect",
22                },
23            ],
24            "edges": [
25                {
26                    "from": "planner",
27                    "to": "payment",
28                    "mode": "DIRECT",
29                },
30                {
31                    "from": "planner",
32                    "to": "worker",
33                    "mode": "DELEGATED",
34                },
35                {
36                    "from": "worker",
37                    "to": "payment",
38                    "mode": "DIRECT",
39                },
40                {
41                    "from": "planner",
42                    "to": "payment",
43                    "mode": "RETRY",
44                },
45            ],
46        },
47        "invariants": [{
48                "id": "spend-cap",
49                "effect": "payment",
50                "operator": "BOUND",
51                "field": "amount",
52                "comparison": "<=",
53                "value": 100,
54                "scope": "cumulative",
55            }],
56        "evidence": [
57            {
58                "invariant_id": "spend-cap",
59                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
60                "surface": "payment",
61                "mode": "DIRECT",
62                "condition": "ABOVE_THRESHOLD",
63                "result": "PASS",
64                "witness_id": "test:DIRECT:ABOVE_THRESHOLD",
65            },
66            {
67                "invariant_id": "spend-cap",
68                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
69                "surface": "payment",
70                "mode": "DELEGATED",
71                "condition": "AT_THRESHOLD",
72                "result": "PASS",
73                "witness_id": "test:DELEGATED:AT_THRESHOLD",
74            },
75            {
76                "invariant_id": "spend-cap",
77                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
78                "surface": "payment",
79                "mode": "DIRECT",
80                "condition": "AT_THRESHOLD",
81                "result": "PASS",
82                "witness_id": "test:DIRECT:AT_THRESHOLD",
83            },
84            {
85                "invariant_id": "spend-cap",
86                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
87                "surface": "payment",
88                "mode": "RETRY",
89                "condition": "AT_THRESHOLD",
90                "result": "PASS",
91                "witness_id": "test:RETRY:AT_THRESHOLD",
92            },
93            {
94                "invariant_id": "spend-cap",
95                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
96                "surface": "payment",
97                "mode": "DELEGATED",
98                "condition": "BELOW_THRESHOLD",
99                "result": "PASS",
100                "witness_id": "test:DELEGATED:BELOW_THRESHOLD",
101            },
102            {
103                "invariant_id": "spend-cap",
104                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
105                "surface": "payment",
106                "mode": "DIRECT",
107                "condition": "BELOW_THRESHOLD",
108                "result": "PASS",
109                "witness_id": "test:DIRECT:BELOW_THRESHOLD",
110            },
111            {
112                "invariant_id": "spend-cap",
113                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
114                "surface": "payment",
115                "mode": "RETRY",
116                "condition": "BELOW_THRESHOLD",
117                "result": "PASS",
118                "witness_id": "test:RETRY:BELOW_THRESHOLD",
119            },
120            {
121                "invariant_id": "spend-cap",
122                "invariant_signature": "4d428aeec56889ddb78341d9c252df15e60131af38ce8c393bea6a30ef4a9606",
123                "surface": "payment",
124                "mode": "RETRY",
125                "condition": "MULTI_ACTION_BELOW",
126                "result": "PASS",
127                "witness_id": "test:RETRY:MULTI_ACTION_BELOW",
128            },
129        ],
130    } }
131
132# Run the Actor and wait for it to finish
133run = client.actor("bono718/agent-invariant-coverage-gap-resolver").call(run_input=run_input)
134
135# Fetch and print Actor results from the run's dataset (if there are any)
136print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
137for item in client.dataset(run.default_dataset_id).iterate_items():
138    print(item)
139
140# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Agent Invariant Coverage Gap Resolver API in Python

The Apify API client for Python is the official library that allows you to use Agent Invariant Coverage Gap Resolver API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Agent Invariant Coverage Gap Resolver API in JavaScript

Agent Invariant Coverage Gap Resolver API through CLI

Agent Invariant Coverage Gap Resolver OpenAPI definition

Agent Invariant Coverage Gap Resolver API

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