As intelligent agents become more efficient, the key question shifts from “Can they act?” to “How will they interact?” This raises the need for infrastructure that enables these agents to collaborate, coordinate, and generate economic value at scale. Agentic blockchains address this challenge. Unlike traditional blockchains, which provide decentralized execution through rigid and entirely on-chain smart contracts, agentic blockchains embed AI capabilities directly into the blockchain. This replacement of static smart contracts with dynamic, intelligent programs allows for more complex functionality. Additionally, they support off-chain agents that register on-chain, enabling discovery, task assignment, and orchestration via the blockchain while operating independently in more suitable environments. Early signs are evident: Open-source projects like AutoGPT demonstrate growing demand for autonomous workflows, and startups are building AI orchestration layers. Coordination among agents is transitioning from theoretical to urgent. This article explores two questions: 1. Why will decentralized agent-based systems define the future of economic coordination? 2. What core challenges exist in constructing the infrastructure for this future? **Rebuilding Economic Infrastructure for Agentic Blockchain** Human economies evolved to reduce transaction costs and enable modular productivity, as explained by Ronald Coase’s theory of the firm, which states companies minimize contract negotiation and execution costs. A comparable pipeline is now needed for machines. Key features required for a decentralized AI agent economy include: - **Make It Clear: How Agents Understand Tasks** Clear intent drives business transactions. In agent ecosystems, specifiability means tasks must be structured, machine-readable, with clearly defined goals, inputs, outputs, constraints, dependencies, and incentives. Specifiability forms the foundation for autonomous cooperation, extending beyond mere metadata. - **Find the Right Fit: From Search to Smart Matchmaking** The evolution from manual search to digital marketplaces now progresses further: agents must discover other agents for task delegation.

This demands semantic discoverability using structured capability profiles, alignment with task definitions, reputation, and performance history. Simple keyword searches are insufficient; agents require meaning-based matchmaking. - **Coordinate Autonomously: Orchestration Without Managers** Rather than project managers and spreadsheets, agents depend on orchestration—task graphs, scheduling, dependency resolution, fault tolerance, and interface compatibility. These blend workflow engines with multi-agent planning frameworks. - **Build Trust With Code: Autonomous Verification** Trust in commerce necessitates built-in verification. This involves cryptographic execution proofs, automated validation rules, domain-specific verifier agents, and manipulation-resistant incentives. Verification agents act as distributed validators confirming task success. - **Close the Loop: Real Payments for Real Work** Value transfer is essential. Agent ecosystems need secure digital payments, conditional rewards tied to task completion, tokenized market incentives, and reputation-based pricing. Payment signals task success, closing the economic loop. **From Blockchains to Agentic Platforms** While blockchains enable decentralized trust, they are not designed for agent coordination. Smart contracts support only simple logic, insufficient for open-ended autonomous system coordination. An AI-native infrastructure requires five core capabilities: - **Proposition Intelligence:** Agents translate ambiguous user goals into precise specifications using language models, symbolic reasoning, and schema enforcement, evolving tasks from vague prompts to formal actions. - **Semantic Discovery:** Discovery mechanisms go beyond indexing by leveraging semantic similarity algorithms, task-capability graph resolution, and context-sensitive rankings to enable meaningful matchmaking. - **Distributed Orchestration:** Instead of centralized control, coordination relies on peer-to-peer delegation, shared task graphs, and resilient network execution, making coordination a shared protocol. - **Agentic Verification:** Each task includes verification logic through reputation voting, zero-knowledge proofs, and replayable logs, embedding trust directly into the infrastructure. - **Demand-Driven Evolution:** Platforms analyze task proposals to identify gaps, incentivizing agents to meet underserved needs and evolve according to demand signals. **Lessons From Building Agentic Infrastructure** Designing such systems reveals key insights: discovery requires shared semantics; verification must balance trust and computation; incentives alone don’t guarantee quality—feedback loops are crucial; vague goals stall progress, so iterative refinement is necessary. Just as firms eased human coordination, we need systems to minimize friction in agent-based value creation. The future agentic economy depends on reimagining five pillars—specifiability, discoverability, orchestration, verification, and monetization—tailored for machine agents.