AI agents are emerging as a direct challenge to the traditional app model by turning software into verified, user-built systems, with blockchain-style verification and attestations serving as the connective tissue between private agents and external services. The shift reframes applications from vendor-delivered binaries into agent-generated workflows that connect only to verifiable endpoints, positioning crypto’s verification tools—not branding—as the decisive layer of trust.

Technology Overview

The current software trust model hinges on running code authored by others—developers, companies, foundations, or anonymous maintainers—delivered through app stores, package managers, or operating systems. Security practices such as permissions, code signing, sandboxing, endpoint detection, vendor due diligence, and incident response exist because the core act remains risky: executing a third party’s instructions on one’s own machine, within one’s own accounts, and against one’s own data.

Recent supply chain incidents have exposed how fragile that bargain can be. The SolarWinds compromise demonstrated that malicious changes seeded inside a trusted build pipeline can propagate through routine updates and reach sensitive institutions across regions. A similar pattern appeared in the XZ Utils backdoor, where malicious code in versions 5.6.0 and 5.6.1 of a widely used compression library altered behavior via a disguised test file and build-process manipulation, ultimately modifying liblzma to intercept and change data interactions. Crypto users have seen comparable risks through DNS and JavaScript npm exploits that deliver compromised code via otherwise ordinary channels.

Industry responses—most notably the NIST Secure Software Development Framework and the SLSA framework—push stronger processes, provenance, and tamper resistance into the pipeline. These measures are necessary, yet they underscore a limit of the current approach: ever-more elaborate methods for choosing which external code deserves trust. The next model aims to reduce how much external code needs to be trusted at all.

Under that model, third-party binaries cease to be the default choice. Instead, users ask a personal agent to build the required function locally and then connect it to external services that can prove what they are and how they operate. The center of gravity shifts from acquiring compiled artifacts to generating auditable implementations that adhere to user-defined policies.

How It Works

Agentic development is already visible in coding agents. OpenAI Codex introduced parallel, cloud-based software engineering capabilities; Anthropic’s Claude Code maps codebases, modifies files, runs tests, and returns committed changes; GitHub’s Copilot coding agent extends that pattern into GitHub workflows across issues and pull requests; and Google Jules presents an autonomous coding system that ingests product context and ships pull requests. While these products are framed as tools for developers, the framing is narrowing: even Codex has shifted toward interfaces that emphasize chat and outputs over raw terminals.

As creation becomes a personal act of delegation, users describe desired workflows and let agents generate interface, logic, integrations, tests, and execution paths. The resulting software can be short-lived or long-lived, regenerated on demand, forked, audited, or discarded. Applications become disposable expressions of intent—local policies compiled into usable surfaces—rather than permanent objects to be installed and maintained.

This transition separates observation from execution. Users can examine other people’s workflows, prompts, data schemas, service integrations, and interface patterns, then instruct their own agents to rebuild the same function from first principles within environments governed by their rules. In this world, distribution looks less like shipping code and more like publishing intent, design artifacts, proofs, schemas, and API expectations. Crypto’s role enters through verification: the user-side agent increasingly connects only to services that attest to their behavior.

Industry Impact

Verifiable endpoints become the new distribution layer. Zero-knowledge systems, exemplified by ZK rollups on Ethereum, show how off-chain computation can be accepted on-chain once a succinct proof validates the resulting state transition. The same logic extends to service interactions beyond transaction scaling: users may demand proofs that an endpoint ran approved code, enforced specific constraints, protected privacy boundaries, or produced outputs from an audited build.

In practice, this points to an agent-controlled operating layer. A user might ask for a dashboard, portfolio tool, research assistant, publishing stack, personal CRM, accounting flow, or security monitor. The agent composes these from generated code and ranked endpoints. Because the agent created the code, it is inspectable. Because the agent selected dependencies under policy, they are constrained. And because the user requires auditability, the execution environment is set up for verification from the start.

This dynamic creates a market for verifiable functions, agent-generated clients, and ranked external services. Third-party developers still publish work, but the emphasis moves to protocols, APIs, templates, proofs, models, components, and reference implementations that user agents can reconstruct. Enterprises remain, yet their advantage shifts from distribution control to reliability proofs. Open-source communities continue to matter, but success depends less on maintainers’ reputations and more on providing structured materials that allow agents to rebuild safely.

Concrete use cases follow the same pattern. A portfolio tracker becomes a generated surface layered over market data endpoints, wallet permissions, tax logic, and reporting rules. A publishing system becomes a generated workflow over identity, editing, content management, analytics, and distribution APIs. A research terminal becomes a generated interface spanning databases, model calls, provenance checks, and private notes. In each case, the agent handles composition while the outside world supplies verifiable resources—and infrastructure providers face a clear test: prove claims, publish interfaces, expose constraints, and let user agents decide whether to include the service.

Future Implications

The defining split is not strictly local versus cloud but private sovereignty versus managed convenience. A private system can still use cloud compute under user-defined constraints. A corporate system can run locally while enclosing identity, permissions, monetization, and telemetry inside a vendor stack. The core questions become: who defines the app, who sets access, who receives telemetry, who dictates upgrades, and who benefits from user dependence?

One path centers on personal software sovereignty. Users maintain agents that build and rebuild the tools they need, selecting endpoints by attestations, cost, reliability, privacy, and alignment. Interfaces can be abandoned without losing workflows. Endpoints can be swapped. Clients can be regenerated when compromised or captured. The software remains portable because the user owns the intent and the agent can reproduce the implementation.

The other path favors managed convenience. Corporate platforms can bundle apps with identity, credits, payments, storage, AI access, and default workflows. Subsidies—whether compute credits, token distributions, or UBI-adjacent income schemes—can become soft lock-in rails. Debates over income distribution have already intersected with proof-of-personhood experiments such as Worldcoin, underlining how economic support, identity verification, compute access, and permissions can converge in ways that appear optional yet steer users toward vendor-managed environments.

Risks accompany both paths. Attackers will probe agents, prompts, dependency selection, model supply chains, and endpoint attestations. Verification layers themselves could harden into chokepoints if captured by a small set of certificate authorities, cloud platforms, or model vendors. Claims of personal sovereignty ring hollow unless users can inspect, migrate, and revoke.

The adoption curve will be uneven. Regulated sectors will move slower. Enterprises will defend app ecosystems through compliance. Many consumers will continue to reach for defaults when private alternatives feel brittle. Yet the direction is clear: as agents make private generation routine—and as endpoints prove their claims—the social calculus around opaque third-party binaries starts to invert. Running someone else’s code will require justification; using an agent to generate and verify one’s own tools will look like the prudent default, even if it means forgoing incentives that keep others plugged into the corporate bundle.