AI infrastructure / Collective intelligence

Collective Intelligence
for Autonomous Agents

Multi-model consensus for critical execution paths.

Before your agents execute a high-stakes plan, Qmesh provisions an evaluation mesh across diverse models in an isolated sandbox, challenging unstated assumptions, reducing single-model bias, and reaching consensus aligned with your intent and bound by your policies.

See how it works Let’s talk

Isolated sandboxes. Diverse models. Collective judgment.

agent@company: ~/ops — bash

~/ops $ cat decision.json
{
  "proposal": "deploy the migration",
  "rationale": "checks pass; rollout is ready",
  "uncertainty": "recovery path is unverified",
  "intent": "ship safely; preserve recovery",
  "risk": "critical",
  "scope": "prod-db-cluster"
}

Click the command or press Enter to follow the decision.

01 / HIGHER RELIABILITY

Surface blind spots. Catch drift before execution.

02 / SOUNDER DECISIONS

Stress-test alternatives. Weigh tradeoffs across models.

03 / STRONGER ALIGNMENT

True to intent. Verified by policy.

One consensus layer / Two ways to begin

A decision to review.
A problem to solve.

Trace a high-stakes proposal through isolated evaluation, cross-model challenge, and policy verification.

qmesh / collective intelligenceScripted example

Change the example Scenario, mesh size & models

Critical decisionMesh sizeModel mixSimulated session · No live model calls

Your company’s agentCOMPANY → QMESH

A plan is ready. The stakes require another look.

Your agent has a proposed deployment and a concise rationale. The recovery risk triggers a call to Qmesh before the agent acts.

The original intent, company policy, and decision authority travel with the request.

WHY IT MATTERS

Surface blind spots and unverified assumptions before execution.

agent@company: ~/ops

$ cat decision.json
{
  "proposal": "deploy the migration",
  "rationale": "checks pass; rollout is ready",
  "uncertainty": "recovery path is unverified",
  "intent": "ship safely; preserve recovery",
  "risk": "critical",
  "scope": "prod-db-cluster"
}

Illustrative session. Select Play session to follow the decision.

Simulated agents and evidence; no model calls. CLI syntax and integrations are illustrative, not a released API. Inspect internal analysis to explore judgments or the collaboration board.

From investigation to verified outcome. The proposed learning loop uses real results to improve future investigations—not agreement alone.

The vision / Collective judgment

Independent minds.
A shared direction.

Intelligence is distributed. The challenge is turning it into reliable collective judgment—without losing sight of human intent.

Align execution with intent.

Agents start from the same objective, identify gaps, and organize investigations as evidence emerges. Responsibilities can evolve without a permanent LLM leader defining everyone’s interpretation.

Surface blind spots early.

Independent first judgments, anonymous challenge, and targeted verification are designed to catch planning mistakes, weak evidence, and policy conflicts before action. Useful minority objections remain visible.

Converge on sounder solutions.

Explore alternatives against your intent, constraints, and resource boundaries. The long-term aim is to learn which agents and checks improve verified outcomes, so future investigations spend effort where it adds value.

A principle that stays fixed

Consensus built on evidence.
Diverse models. Sandbox-tested. Policy-bound.

“True to intent. Verified by policy.”

Inside Qmesh / Proposed infrastructure

A collective intelligence layer
for agent systems.

Connect your existing agent system or provision an on-demand evaluation mesh around a critical decision.

01 / PARTICIPATION

Choose how agents join.

Select a diverse model mix and provision isolated evaluators—each running with a clean context and its own writable sandbox.

models: [A × 4, B × 4, C × 4]
context: isolated_per_node
workspace: isolated
policy: enforced_at_runtime

Illustrative configuration. API, MCP, and runtime integrations are part of the proposed product.

02 / COLLECTIVE JUDGMENT

Qmesh Collective Intelligence

Independent contributions → anonymous challenge → focused verification → explicit decision rules.

Original intentPermissionsRequired checksScope boundaries

03 / DECISION RECORD

A result you can inspect.

Recommendation and alternatives
Evidence and material objections
Unresolved conditions
Authority to act or escalate
Execution and outcome history

Uncertainty remains visible

Separate runs. Diverse perspectives. Evaluators run across your selected model mix without copying weights. Isolated contexts protect independent first judgment, while cross-model checks surface shared blind spots.

Security by design / Sandboxed exploration

Explore in isolation.
Act within boundaries.

Each evaluator runs in an isolated environment with zero shared memory. The evaluation mesh brings diverse models together around a single proposal while keeping their working state strictly separated.

sandbox / illustrative layout

node_01Fresh context/workspace/01

node_02Fresh context/workspace/02

node_03Fresh context/workspace/03

Shared objective · Separate histories · Isolated writable workspaces

Scope the tools.

Limit data access, network access, and external actions to what the task requires.

Keep policy in the execution path.

Required checks and company authorization govern action, regardless of the vote.

Make the process accountable.

Retain agent provenance, evidence, objections, and the basis for acting or escalating.

These are design boundaries for the proposed runtime. This website does not provision live sandboxes.

Where it applies

Different problems.
The same need for judgment.

Explore where independent perspectives, productive disagreement, and verifiable checks could change a decision.

SOLUTION DISCOVERY / A SHARED PROBLEM

Find a solution worth acting on.

Give a sandboxed mesh your problem, intent, and constraints. Follow models as they investigate, challenge, combine, and test approaches.

Explore the solution journey →

ENGINEERING & INFRASTRUCTURE

Question the plan
before the deployment.

Investigate assumptions, test rollback paths, and verify a change against your reliability requirements.

Follow the decision →AUTONOMOUS OPERATIONS

Resolve uncertainty
before taking action.

Challenge conflicting signals and check a response against permissions, continuity, and the cost of being wrong.

Follow the decision →RESEARCH & ANALYSIS

Test the conclusion
against the evidence.

Distinguish repeated claims from independent sources. Preserve a useful objection when the evidence is incomplete.

Follow the decision →

Our standard of proof

More agents should
earn their place.

The goal is better judgment and fewer decision errors. That needs to be demonstrated against strong alternatives, with comparable information, budgets, and policy controls.

COMPARE WITH

A strong single agent

Independent attempts + voting

A fixed review team

MEASURE

Decision errors & useful corrections

Intent alignment & policy compliance

Total cost, latency & human review

These are evaluation goals. Qmesh is in development; this site makes no measured performance claims.

A little more context

Questions worth asking.

What is Qmesh being built to do?

Qmesh is AI infrastructure for collective intelligence. It is designed to evaluate proposals independently across diverse models, challenge unstated assumptions, and reach consensus aligned with human intent and bound by policy.

Do I need an existing team of agents?

No. The proposed sandbox capability provisions an on-demand evaluation mesh across your selected model mix. Each evaluator starts with an isolated context and writable workspace. Connecting existing agents is another planned participation path.

How is alignment different from agreement?

Agreement describes how agents’ judgments compare. Alignment here means serving the original human intent while respecting company constraints, permissions, and required checks. A unanimous vote cannot establish correctness or override those boundaries.

Can I use the production platform today?

Qmesh is in development. The demonstrations on this page use scripted data and do not run live agents. Get in touch to discuss the vision, an integration, or a problem that could help shape the infrastructure.

Let’s build what comes next

Better judgment is
where it begins.

Building agent systems? Exploring collective intelligence?
Bring a problem, an integration idea, or a different perspective.

Tell us about your question or idea.

Collective Intelligencefor Autonomous Agents

A decision to review.A problem to solve.

Is this deployment ready to proceed?