Introduction

Early Release (v0.1.x) — Attesta is under active development. The core API is functional and tested, but interfaces may change between minor versions. Pin your dependency to a specific version in production. Report issues through this repository’s issue tracker.

Attesta = Attestation + Gate. Every AI agent action passes through a trust gate before it touches the real world.

The Problem: Approval Fatigue

Every AI agent framework has a human-in-the-loop mechanism. LangChain has HumanApprovalCallbackHandler. CrewAI has human_input=True. OpenAI Agents SDK has handoffs. They all do the same thing: pause execution and ask a human to click Yes or No. A human who sees 200 approval prompts a day stops reading them. Approval fatigue turns your safety layer into a rubber stamp.

Capability	Baseline HITL	Attesta ✓
Risk awareness	None — all actions get the same Y/N	5-factor scoring auto-detects risk from function signature
Challenge depth	Binary confirm for everything	Scales by risk: auto-approve → confirm → quiz → teach-back → multi-party
Approval fatigue	High — every action prompts	Low — only risky actions surface; safe actions pass silently
Audit integrity	Basic logs (if any)	SHA-256 hash-chained JSONL; tamper-evident
Multi-party support	No	CRITICAL actions require 2+ independent approvers
Trust adaptation	No	Bayesian trust engine; incidents instantly revoke trust
`drop_database()` outcome	”Confirm? [Y/N]” → instant click → executed	Score 0.95 CRITICAL → multi-party review, 30s+ minimum, teach-back + quiz

_{Illustrative comparison; baseline behavior varies by framework.}

The Attesta Pipeline

AI agents can write code, execute shell commands, manage infrastructure, and access sensitive data. Attesta ensures no high-risk action happens without informed human approval — while letting low-risk actions fly through silently.

Attesta approval pipeline: AI Agent → Risk Scoring → Trust Engine → Challenge Branching (LOW/MEDIUM/HIGH/CRITICAL) → Decision → Audit Trail → Trust Feedback Loop

Core Pillars

Risk-Aware Scoring

A 5-factor scoring engine analyzes the function name, arguments, docstring, caller hints, and call novelty. get_user() scores 0.1. drop_database() scores 0.95. The challenge scales accordingly — no manual risk tagging required.

Escalating Challenges

Low-risk actions pass through silently. High-risk actions require the approver to answer auto-generated comprehension questions. Critical actions require multiple independent approvers, each completing a different challenge type. You can’t just click through.

Tamper-Proof Audit

Every decision is recorded in a SHA-256 hash-chained log. Modify any entry and every subsequent hash breaks. attesta audit verify checks the entire chain. attesta audit rubber-stamps flags suspiciously fast approvals.

Adaptive Trust

A Bayesian trust engine tracks each agent’s approval history with exponential decay. Trusted agents see reduced friction over time. A single security incident instantly penalizes trust. Critical actions are immune to trust discounts — always fully gated.

What Makes Attesta Different

Risk is computed, not assumed — The scorer auto-detects risk from the function signature. No manual tagging.
Challenges test comprehension — Quiz and teach-back challenges force the approver to prove they understand the action.
Minimum review times — A wall-clock minimum prevents instant approvals. 3s for medium, 10s for high, 30s+ for critical.
Trust is earned — Agents build trust through consistent safe behavior. Incidents instantly revoke it.

Quick Example

from attesta import gate

@gate
def deploy(service: str, version: str) -> str:
    """Deploy a service to production."""
    return f"Deployed {service} v{version}"

# Attesta intercepts, scores risk, and prompts for approval
deploy("api-gateway", "2.1.0")

import { gate } from "@kyberon/attesta";

const deploy = gate(async (service: string, version: string) => {
  return `Deployed ${service} v${version}`;
});

// Attesta intercepts, scores risk, and prompts for approval
await deploy("api-gateway", "2.1.0");

Risk Levels at a Glance

Score	Level	Default Challenge	Example
0.0 - 0.3	LOW	Auto-approve	`get_user_profile()`
0.3 - 0.6	MEDIUM	Confirm (Y/N)	`send_email()`
0.6 - 0.8	HIGH	Quiz (comprehension)	`deploy_service()`
0.8 - 1.0	CRITICAL	Multi-party approval	`drop_database()`

Works With

Code Frameworks
No-Code Platforms
Domain Profiles
Languages

LangChain, OpenAI Agents SDK, Anthropic Claude, CrewAI, MCP, Vercel AI SDK

Custom domain profiles via register_preset() — define industry-specific risk patterns, escalation rules, and compliance references

Next Steps

Quickstart

Get up and running in 5 minutes

OSS vs Cloud

See the public feature boundary

How It Works

Understand the 4-stage approval pipeline

Integrations

Connect Attesta to your AI framework

Configuration

Customize policies, risk scoring, and trust

Getting Started

Core Concepts

Configuration

Domain Profiles

Examples

Guides

TypeScript

Resources

The Problem: Approval Fatigue

The Attesta Pipeline

Core Pillars

Risk-Aware Scoring

Escalating Challenges

Tamper-Proof Audit

Adaptive Trust

What Makes Attesta Different

Quick Example

Risk Levels at a Glance

Works With

Next Steps

Quickstart

OSS vs Cloud

How It Works

Integrations

Configuration

​The Problem: Approval Fatigue

​The Attesta Pipeline

​Core Pillars

Risk-Aware Scoring

Escalating Challenges

Tamper-Proof Audit

Adaptive Trust

​What Makes Attesta Different

​Quick Example

​Risk Levels at a Glance

​Works With

​Next Steps

Quickstart

OSS vs Cloud

How It Works

Integrations

Configuration

The Problem: Approval Fatigue

The Attesta Pipeline

Core Pillars

What Makes Attesta Different

Quick Example

Risk Levels at a Glance

Works With

Next Steps