Trustworthy Collective Intelligence

Enabling secure, real-time collaboration across trust boundaries through trustworthy and verifiable multi-agent systems

Intelligence across trust boundaries

Despite large scale digitization over the last decade, high Coasean transaction costs, arising from negotiation, contracting, and coordination continue to constrain efficiency and innovation in complex economies such as India. Agentic AI offers a powerful opportunity to reduce these costs by enabling agents to autonomously act and coordinate on behalf of humans and organizations. However, these systems will succeed only if we solve fundamental challenges of multi-agent coordination and build infrastructure that makes cross-organization trust possible.

At SPARC, our mission is to make technological infrastructure (new communication protocols, safety benchmarks, scenario-simulations and new foundational models) that promises to make agent-to-agent interactions effective, safe and trustworthy.

What is trustworthy collective intelligence?

Trustworthy collective intelligence refers to systems where:

  • Autonomous agents represent organizations
  • Agents coordinate decisions across institutional boundaries
  • Interactions are governed by enforceable policies and commitments
  • Trust is established through verifiable infrastructure


This enables coordination at scale while preserving decentralization and organizational autonomy.

Collective intelligence is intelligence that emerges out of interactions between AI systems.

Research focus

World models for
coordination
Create probabilistic world models that allow agents to represent uncertainty, track system state, and reason about future outcomes of interaction.
Secure coordination
protocols

Develop protocols that enable agents to coordinate across trust boundaries while preserving data sovereignty and minimizing exposure.

Verifiable trust
infrastructure
Design cryptographic identity, commitment schemas, and secure execution environments that make cross-organization interactions enforceable and auditable.
Multi-agent
evaluation systems
Build shared environments and metrics to evaluate coordination efficiency, robustness, and adherence to policy in open, multi-agent systems.
Join academic, industry, and public-sector partners to build the foundations of trustworthy collective intelligence.