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Infrastructure for agentic decision-making

Decisions are moving from people to agents. Enterprise decision-making is increasingly driven by agents planning and acting autonomously across numerous tools. This shift promises significant gains in speed and scale but leaves an enterprise with a fragmented record of how its own decisions are made.

We build infrastructure to return control of the decision-making record to enterprises. By giving them the power to govern and learn from their agentic decision ledger, DSI helps enterprises improve their decision-making and unlock the full value of their investments in AI.

Pathiqa controls the relationship between agents and enterprise information. Exostrate controls the runtime in which agents execute. Decision Superintelligence Labs develops the formal foundations for safe delegation to autonomous systems.

Pathiqa · ProductExplore Pathiqa →

Governed infrastructure for enterprise AI integration

Neutral infrastructure between an enterprise’s systems of record and its systems of action. It integrates any AI tool securely across an organisation’s data, wherever it lives, with every action written to an immutable ledger.

// The ontology

Resolves the enterprise’s data by meaning and persists independently of the systems it describes.

// The ledger

Records every declared intent, access, action, and outcome as a signed, immutable entry.

// The key

The unit to which scope, access rights, and reputation attach, internally and across boundaries.

Views · PathiqaSee Views →

Something you can actually see.

A View is a shareable, continuously updating window onto your enterprise. It combines the numbers with an agent’s read of them, surfaces what you didn’t know to ask, and answers follow-up questions in place. The first View is the Agent Stand-Up: a daily brief from your agents on what they did, what they are unsure about, and what needs a human.

Agent Stand-Up

Treasury Ops

Live · updated 2 min ago

142Invoices resolved

3Exceptions

68%Spend vs budget

// agent · operational assessment

Cash position is healthy. Two counterparties, ACME and Northwind, are slow to confirm, so I’ve queued ACME for manual review. One refund exceeds my policy limit and needs a human decision.

confidence: medium

⚑ 1 refund over policy limit, awaiting your decision

Exostrate · ProductExplore Exostrate →

The agent hypervisor

Ship autonomous AI like you ship containers: governed, isolated, lease-bound, and cryptographically auditable. Package an agent, push it to a Git-coupled registry, lease it into an isolated runtime under a hard budget, and prove every action it takes.

// 01 · Package

Bundle an agent, its dependencies, and its policy into a versioned ExoChart.

// 02 · Push

An immutable content digest and Git provenance, traceable to the exact commit.

// 03 · Lease

An isolated runtime: partitioned compute, encrypted storage, a hard token budget.

// 04 · Govern

Watch it execute, authorize every action, and prove it all against a tamper-evident ledger.

ResearchRead the research →

Building the foundations for trusted AI agency

The formal, technical, and organisational conditions under which consequential work can be delegated to AI while preserving authority, accountability, and control, across four programme areas.

// 01

Agent governance and control

Permitting exactly the next actions that keep an acceptable completion reachable, under partial observation and authority that changes with runtime state.

// 02

Proof-carrying authority

Certificates that a governing decision was both authorised and exactly effective, and governance memory in which an earlier certified decision survives everything recorded after it.

// 03

Design and reliability

Separating what an agentic system may do from what it does. Potential, realization, minimal permission, and selection under cost, latency, exposure and risk.

// 04

Organisational adoption

Adoption as a state rather than a score: sustained delegation of consequential work, the evidence loop that holds it in place, and the thresholds that govern entry and exit.

Ready to useExplore Pathiqa →

Ask an agent in a few lines.

Pathiqa ships idiomatic clients for every stack. Point an agent at a question and get an answer grounded in your governed data, with every access written to the immutable ledger. The same call, in your language.

from pathiqa import Pathiqa

client = Pathiqa()

response = client.ask(
    agent="ag_1e6477e5812e424ef9d087effa4670ec",
    question="How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?",
)

print(response.output_text)

import { Pathiqa } from "pathiqa";

const client = new Pathiqa();

const response = await client.ask({
  agent: "ag_1e6477e5812e424ef9d087effa4670ec",
  question: "How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?",
});

console.log(response.outputText);

import ai.pathiqa.Pathiqa;
import ai.pathiqa.models.AskParams;
import ai.pathiqa.models.AskResponse;

Pathiqa client = new Pathiqa();

AskResponse response = client.ask(AskParams.builder()
    .agent("ag_1e6477e5812e424ef9d087effa4670ec")
    .question("How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?")
    .build());

System.out.println(response.outputText());

package main

import (
    "context"
    "fmt"

    "github.com/pathiqa/pathiqa-go"
)

func main() {
    client := pathiqa.NewClient()

    response, err := client.Ask(context.Background(), pathiqa.AskParams{
        Agent:    "ag_1e6477e5812e424ef9d087effa4670ec",
        Question: "How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?",
    })
    if err != nil {
        panic(err)
    }

    fmt.Println(response.OutputText)
}

use pathiqa::Pathiqa;

let client = Pathiqa::new();

let response = client
    .ask()
    .agent("ag_1e6477e5812e424ef9d087effa4670ec")
    .question("How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?")
    .send()
    .await?;

println!("{}", response.output_text);

require "pathiqa"

client = Pathiqa::Client.new

response = client.ask(
  agent: "ag_1e6477e5812e424ef9d087effa4670ec",
  question: "How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?"
)

puts response.output_text

use Pathiqa\Client;

$client = new Client();

$response = $client->ask(
    agent: 'ag_1e6477e5812e424ef9d087effa4670ec',
    question: 'How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?',
);

echo $response->outputText;

using Pathiqa;

var client = new PathiqaClient();

var response = await client.AskAsync(
    agent: "ag_1e6477e5812e424ef9d087effa4670ec",
    question: "How have time-of-day and day-of-week purchasing patterns changed since the launch of our new ad campaign in Tokyo over the last 3 weeks?"
);

Console.WriteLine(response.OutputText);

News

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Company

Decision Superintelligence is the company. Pathiqa and Exostrate are its products. Decision Superintelligence Labs is its research programme, and DSI Solutions its advisory and implementation practice.

DSI is hiring. Enquiries regarding the work, the research programme, or collaboration are welcome.

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