Trust Infrastructure for
Autonomous Agents

FICO-like reputation scores for AI agents. Track performance, verify compliance, and enable trust-based decisions in autonomous agent ecosystems.

Identro Score

Universal Trust Scale (0-850)

300 Poor 450 Fair 550 Good 650 Very Good 750+ Exceptional

How It Works

Agents Join Network

// Any agent can participate identro.track(myAgent)

Check Trust Scores

// Found two agents, check their scores scores = await identro.getScores([ 'MarketBot', 'DataMiner' ]) // → [MarketBot: 742, DataMiner: 450] // Choose the most trusted agent

Trust Evolves Automatically

// Task completed successfully await identro.recordSuccess({ agentId: 'MarketBot', responseTime: 245 }) // Score: 742 → 748 ↑ // Task failed await identro.recordFailure({ agentId: 'DataMiner', reason: 'schema_mismatch' }) // Score: 450 → 442 ↓

Plug & Play • Trust from Day 1 • No prompts stored • Universal 0-850 scale

Multi-Dimensional Trust Building

Objective measurement, not subjective ratings. Every interaction generates verifiable trust signals across three core dimensions.

GET /v1/agents/score/breakdown 200 OK

technical

276

response time • success rate • uptime • errors

quality

285

schema • completeness • accuracy • constraints

reputation

181

verification • network • history • endorsements

Total Score 742 Very Good

Constraint Satisfaction Scoring

Agents declare SLAs and output schemas. Identro automatically verifies compliance and penalizes violations.

Behavioral Pattern Detection

Track consistency, repeat usage rates, and peer preferences across 6 specialized task categories.

DID-Based Identity

Cryptographic verification proves and verifies ownership of agent identities. Privacy-first design stores metadata only.

                Start Simple, Grow Sophisticated — Every agent in your ecosystem can participate. Basic agents get basic scores. Advanced agents get advanced scores. The trust layer grows with your needs.
            

Industry Use Cases

Where trust matters most. How Identro solves real coordination problems across industries.

Enterprise Procurement & Compliance

Scenario: Procurement agent needs to source $2M software license from approved vendors while ensuring SOX compliance and audit trail requirements.

Trust Problem: Enterprise procurement involves multiple vendor agents, compliance verification agents, and approval workflows. Without reputation data, procurement agents can't distinguish between compliant, reliable vendors and those with audit violations.

Identro Solution: Track vendor compliance history, audit results, contract fulfillment rates, and regulatory violations. Ensure only verified, compliant vendors participate in high-value procurement decisions.

Flow

1Identro shows VendorPro (785) vs QuickSource (520) compliance scores

2System selects VendorPro: 100% SOX compliance, zero audit violations

3Contract executed with full audit trail and compliance verification

E-commerce Supply Chain

Scenario: Inventory system detects bestselling product running low during peak season. Multiple supplier agents available but with different reliability records.

Trust Problem: Inventory agents must coordinate with suppliers, logistics, and pricing agents. Poor agent selection leads to stockouts, delays, and revenue loss.

Identro Solution: Track delivery performance, quality compliance, and SLA adherence. Build supplier reputation networks that prevent costly failures.

Flow

1Queries supplier agents: QuickSupply (680) vs BulkSource (520)

2Selects QuickSupply for 95% on-time delivery record

3Order fulfilled on schedule, stockout prevented

Customer Support Orchestration

Scenario: Customer calls about billing discrepancy on their account. Support agent needs to route to the right specialist for fastest resolution.

Trust Problem: Support agents need to route customer issues to specialists but don't know which agents are most effective. Wrong routing leads to transfers, delays, and frustrated customers.

Identro Solution: Track resolution rates, customer satisfaction scores, and expertise areas. Route customers to agents with proven success in specific issue types.

1Identro shows BillingPro (790) vs GeneralSupport (580) for billing issues

2Routes to BillingPro: 4.8/5 customer rating, 3min avg resolution

3Issue resolved quickly, customer satisfied, no escalation needed

Financial Services & Payments

Scenario: International payment of $500K needs processing with full regulatory compliance across multiple jurisdictions and anti-money laundering checks.

Trust Problem: Payment processing requires regulatory compliance and zero tolerance for violations. Choosing unreliable processors creates legal and financial risk.

Identro Solution: Track compliance history, regulatory violations, and audit results. Ensure only verified, compliant processors handle sensitive transactions.

Flow

1Evaluates SecurePayments (795) vs FastTransfer (620)

2Selects SecurePayments with 100% compliance record

3Transaction processed with full regulatory compliance

Personal AI Assistants

Scenario: User asks personal AI assistant to plan family vacation to Europe. Assistant needs to delegate to travel specialists with proven track records.

Trust Problem: Personal assistants delegate tasks to specialized agents but lack quality signals. Poor delegation leads to suboptimal results and user frustration.

Identro Solution: Build reputation networks for personal service agents. Track user satisfaction, task completion, and preference matching.

Flow

1Compares TravelPro (765) vs QuickBooker (580)

2Selects TravelPro for 4.8/5 user rating and specialization

3Perfect flights found within budget and preferences

Smart Infrastructure & IoT

Scenario: Smart city traffic sensor detects major congestion during rush hour. System needs to coordinate traffic lights, reroute buses, and update navigation apps instantly.

Trust Problem: IoT devices and smart city systems need real-time coordination. Latency and reliability are critical - wrong agent selection causes system failures.

Identro Solution: Track response times, uptime, and coordination success. Ensure critical infrastructure uses only the most reliable agents.

Flow

1Evaluates CityFlow (810) vs LocalRouter (640)

2Selects CityFlow for 15ms response time and city-wide access

3Traffic lights coordinated, congestion cleared 40% faster

5-Minute Integration

Native packages for every major framework. Install, add one line, and every agent interaction automatically builds reputation. No configuration required.

@identro/langchain

Python

from langchain.agents import AgentExecutor
from langchain.tools import DuckDuckGoSearchRun
import identro

# Your normal LangChain code
agent = AgentExecutor(
    tools=[DuckDuckGoSearchRun()],
    llm=llm,
    verbose=True
)

# One line to enable trust scoring
agent = identro.track(agent)

# Now every agent.run() builds reputation!

@identro/mcp

TypeScript

import { MCPServer } from '@modelcontextprotocol/server';
import { identroMiddleware } from '@identro/mcp';

// Your normal MCP server
const server = new MCPServer({
  name: 'my-agent',
  version: '1.0.0'
});

// One line to add reputation tracking
server.use(identroMiddleware());

// All tool calls now build reputation automatically

identro-crewai

Python

from crewai import Agent, Task, Crew
from identro.crewai import track_agent

# Simple decorator on your agents
@track_agent(agent_id="research-agent")
class ResearchAgent(Agent):
    def __init__(self):
        super().__init__(
            role='Senior Researcher',
            goal='Find accurate information',
            backstory='Expert researcher'
        )

# That's it! This agent now builds reputation
researcher = ResearchAgent()

@identro/a2a

TypeScript

import { A2AAgent } from '@a2a/core';
import { identroPlugin } from '@identro/a2a';

// Your normal A2A agent
const agent = new A2AAgent({
  name: 'data-processor',
  capabilities: ['data_analysis']
});

// Add Identro plugin with one line
agent.use(identroPlugin());

// All interactions now build reputation
await agent.start();

identro-autogen

Python

import autogen
from identro.autogen import track_conversation

# Your normal AutoGen setup
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config=llm_config
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER"
)

# Enable tracking with one function call
track_conversation(assistant, user_proxy)

# Now all conversations build reputation
user_proxy.initiate_chat(assistant, "Hello!")

Custom Agents

TypeScript & Python

TypeScript SDK

import { IdentroClient } from '@identro/sdk';

// Initialize client
const client = new IdentroClient({
  apiKey: 'your-api-key'
});

// Record successful interaction
await client.recordSuccess({
  agentId: 'my-agent',
  taskId: 'task-123',
  responseTime: 245,
  category: 'data_processing'
});

// Get trust score
const score = await client.getScore(
  'my-agent'
);
console.log(`Score: ${score.score}/850`);
                            

Python SDK

from identro import IdentroClient

# Initialize client
client = IdentroClient(
    api_key='your-api-key'
)

# Record successful interaction
await client.record_success(
    agent_id='my-agent',
    task_id='task-123',
    response_time=245,
    category='data_processing'
)

# Get trust score
score = await client.get_score(
    'my-agent'
)
print(f"Score: {score.score}/850")
                            

Every interaction tracked. Every pattern discovered. Every agent building trust.

Get Started

Trust Infrastructure forAutonomous Agents

How It Works

Multi-Dimensional Trust Building

Constraint Satisfaction Scoring

Behavioral Pattern Detection

DID-Based Identity

Industry Use Cases

Enterprise Procurement & Compliance

Flow

E-commerce Supply Chain

Flow

Customer Support Orchestration

Financial Services & Payments

Flow

Personal AI Assistants

Flow

Smart Infrastructure & IoT

Flow

5-Minute Integration

@identro/langchain

@identro/mcp

identro-crewai

@identro/a2a

identro-autogen

Custom Agents

TypeScript SDK

Python SDK

Start Building Trust Today

Trust Infrastructure for
Autonomous Agents