Align the work with the goal.
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.
AI infrastructure / Collective intelligence
Better judgment.
Aligned action.
Collective Intelligence Board
Reduce agentic errors. Find better solutions. Qmesh is building sandboxed agent swarms to explore alternatives, challenge assumptions, and optimize outcomes against your goals and policies—the wisdom of the swarm in action.
Sandboxed twins. Independent thinking. Collective judgment.
collective.judgmentScripted illustration
Infrastructure / Deployment
Is this deployment ready to proceed?
SupportUncertainOpposeOne line = one agent
One line = one agent · Swipe the graph to see all five axes →
Ready · 10-second walkthrough
Independent round7 support · 2 uncertain · 3 oppose
A majority supports it. Three agents see a risk.
Separate first judgments expose a rollback concern before the group sees each other’s answers.
Original objective: ship the change without losing the ability to recover.
Simulated agents and evidence; no model calls. This is a closed-round operator view. Peer submissions stay hidden during independent work.
01 / FEWER AGENTIC ERRORS
Challenge assumptions before action.
02 / BETTER SOLUTIONS
Compare alternatives against your goals.
03 / STRONGER ALIGNMENT
Keep policy and permissions binding.
The value / Follow one decision
From a shared goal
to a decision worth acting on.
The wisdom-of-the-crowd approach, applied to AI agents. Explore the flow to see where collective intelligence can add value.
Learn from verified outcomes. Improve which agents, checks, and investigations are used next.
Customer value / Stronger alignment
Every agent starts from the original objective and company policy. Success criteria, permissions, and spending limits stay visible as the work evolves.
Proposed product flow. The interactive examples use scripted data.
The vision / Wisdom of the swarm
Independent minds.
A shared direction.
Intelligence is distributed. The challenge is turning it into useful collective judgment—without losing sight of the human goal.
Reduce agentic errors.
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.
Optimize for better solutions.
Explore alternatives against your goals, constraints, and budget. 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
Agreement is a signal.
Evidence and human constraints govern action.
Inside Qmesh / Proposed infrastructure
A collective intelligence layer
for agent systems.
Bring an existing agent configuration or assemble a temporary board around one problem. No existing agent fleet is required.
01 / PARTICIPATION
Choose how agents join.
Reuse a supported agent configuration. Select one model or a mix, then give each twin a fresh context and isolated writable workspace.
models: [A × 4, B × 4, C × 4]
context: fresh_per_twin
workspace: isolated
budget: set_before_launchIllustrative configuration. API, MCP, and runtime integrations are part of the proposed product.
02 / COLLECTIVE JUDGMENT
Collective Intelligence Board
Independent contributions → anonymous challenge → focused verification → explicit decision rules.
Original goalPermissionsRequired checksCost limits
03 / DECISION RECORD
A result you can inspect.
- Recommendation and alternatives
- Evidence and material objections
- Unresolved conditions
- Authority to act or escalate
- Cost and outcome history
Separate runs. Shared blind spots. Twins reuse the selected model; they do not require a copy of its weights. Fresh contexts protect independent first work, but tests and diverse evidence still matter.
Security by design / Sandboxed exploration
Explore in isolation.
Act within boundaries.
An agent twin is a separate run of a supported agent configuration. A swarm brings those twins together around one goal. The proposed sandbox keeps their working state apart.
sandbox / illustrative layout
twin_01Fresh context
/workspace/01twin_02Fresh context
/workspace/02twin_03Fresh context
/workspace/03Shared 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.
ENGINEERING & INFRASTRUCTUREQuestion the plan
Resolve uncertainty
Test the conclusion
Question the plan
before the deployment.
Investigate assumptions, test rollback paths, and evaluate a change against the original reliability goal.
Explore the illustration →AUTONOMOUS OPERATIONSResolve uncertainty
before taking action.
Challenge conflicting signals and check a response against permissions, continuity, and the cost of being wrong.
Explore the illustration →RESEARCH & ANALYSISTest the conclusion
against the evidence.
Distinguish repeated claims from independent sources. Preserve a useful objection when the evidence is incomplete.
Explore the illustration →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
These are evaluation goals. Qmesh is in development; this site makes no measured performance claims.Decision errors & useful corrections
Goal achievement & policy compliance
Total cost, latency & human review
A little more context
Questions worth asking.
What is Qmesh being built to do?
Qmesh is AI infrastructure for collective intelligence. Its Collective Intelligence Board is designed to help agents preserve independent thinking, organize investigations, deliberate anonymously, and reach decisions within human-defined objectives and boundaries.
Do I need an existing team of agents?
No. The proposed sandbox capability assembles a temporary board from a supported agent configuration and your selected model or model mix. Each twin starts with its own 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 goal 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
A shared goal is
where it begins.
Building agent systems? Exploring collective intelligence?
Bring a problem, an integration idea, or a different perspective.