MuleRun 2.0 and the rise of the AI labor app store

The app store for digital labor just arrived

On November 12, 2025, MuleRun rolled out Version 2.0 and looked less like a neat demo gallery and more like an operating marketplace for work. The company highlighted a surge of creators, curated vertical bundles, and ready-to-run agent teams, positioning itself as a real distribution layer for large language model work rather than another sandbox. See the details in the company’s own MuleRun 2.0 launch announcement.

Why does this matter? For two years, AI agents mostly lived in conference videos and weekend hackathons. What changes adoption is distribution. Once there is a place to find, try, buy, and govern agents, they spread inside companies the way mobile apps spread after app stores appeared. MuleRun 2.0 signals that agent marketplaces are ready to play that role.

From novelty to distribution layer

The idea of an agent app store feels familiar because we have seen this movie before. The web organized pages. Mobile organized apps. Now agents package intent, tools, and policies into a unit that can be installed and paid for. A marketplace turns that unit into a product with discovery, versioning, billing, and reviews.

What makes this different from past plugin catalogs is the focus on outcomes rather than features:

• Agents ship with outcome contracts, not just capabilities. A financial research agent delivers a formatted report and supporting sources, not a bundle of endpoints.
• Trial becomes trivial. One click spins up a run, so evaluation feels like trying a ride share rather than scoping a multi-week software trial.
• Pricing aligns with work. Per-run, per-step, or cost-plus models let creators sell outcomes and let buyers map spend to business value.

When these basics exist, agents stop being a novelty and start becoming deployable digital labor.

The startup model: creator monetization and vertical bundles

If you want to see the startup playbook, MuleRun’s structure is instructive. Creators can set pricing by run, by minute, by step, or cost-plus, with a defined platform fee and the rest going to the creator. That gives makers a reason to keep shipping, and it gives customers a clear way to compare value.

Version 2.0 emphasizes two levers that matter in the near term:

• Versioning as trust. Buyers care that a sales outreach agent on v1.8 fixed the data leak it had in v1.6. Clear version tags and change logs reduce perceived risk and help procurement get to yes faster.
• Vertical bundles. Instead of a generic assistant, marketplaces now offer curated stacks like Ecommerce Design, Investment Research, or Support Automation. Packaged agents share context schemas and outputs, which reduces setup cost and improves first-run success.

The mechanism is simple: tighter packaging lowers activation energy. You can picture an internal champion at a mid-market retailer buying a pre-bundled Catalog Cleanup pack that includes a listing rewriter, an image retoucher, and a syndication checker, all tuned to the same product schema. Day one value replaces month one scoping.

For builders, this dovetails with what we have seen as teams make multi-agent apps shippable. The path to product-market fit is clearer when deployment, versioning, and analytics are not afterthoughts.

The incumbents move in

Startups are not alone. In March 2025, Salesforce launched AgentExchange, a marketplace baked into its Agentforce platform, with a roster of partners and security reviews that mirror the company’s AppExchange model. The motive is clear. Salesforce wants an ecosystem where trusted third parties list agent actions and templates that plug into customer data and workflows. Read the primary details in the Salesforce AgentExchange announcement.

GitHub’s recent Agent HQ points in a similar direction for software teams. Instead of forcing developers to pick one coding agent, GitHub is turning itself into a hub that hosts and governs many, complete with mission control, policy controls, and metrics. It is not a consumer marketplace in the classic sense, but it is the same distribution logic: centralize discovery, standardize controls, and make it easy to try multiple agents on real work.

The contrast is helpful. Startup marketplaces optimize for growth loops and creator economics. Incumbent marketplaces optimize for governance, compliance, and deep integration. Both models accelerate adoption, but they win different customers and use cases.

Why this unlocks adoption now

Three ingredients are lining up in late 2025.

1) Common plumbing

Model Context Protocol gives builders a standard way to plug agents into tools and data. Think of it as a port that connects a sales agent to your CRM or a research agent to your document store without a tangle of custom adapters. As more runtimes and editors support MCP, the integration cost per agent falls, and marketplaces gain supply and demand at the same time.

2) Better guardrails and evals

Agent demos die when they hallucinate invoices or send emails to the wrong list. The current generation of agent runtimes supports input validation, policy checks, reversible actions, and offline dry runs. Pair that with eval suites that score agents on accuracy, cost, latency, and safety, and a marketplace can require a passing score before listing. That turns trust us into a measurable bar.

3) Analytics that tie to money

Every executive asks the same thing: where is the return. Marketplace hosts now track run-level metrics by user, by data source, by action type, and by objective outcome. The jump from curiosity to budget happens when a buyer can say, This reconciliation agent closed our month two days faster and cost 340 dollars this quarter.

Put those three together, and agents are no longer just exciting. They are purchasable.

What startup builders should do in the next 90 days

Time favors the teams who operationalize. Here is a focused playbook.

Ship on an open standard

Implement MCP clients or servers first so your agent can move between marketplaces and enterprise runtimes without custom glue. This lowers sales friction and increases your optionality when vendor priorities shift.

Choose pricing that matches value

If your output is a discrete deliverable, per-run pricing simplifies the buying decision. If cost varies by data size or tool calls, cost-plus with a transparent margin builds trust. Avoid unit prices buyers cannot relate to. Per thousand tokens makes non-technical buyers tune out.

Treat versioning as a product surface

Maintain a public change log, semantic versions, and a safe mode configuration that reverts to a known good policy set. Make it easy for buyers to pin a version and to roll forward intentionally. Your release notes are as much a sales asset as your demo.

Package for verticals

Publish a core agent and a variant customized to a specific niche with prefilled prompts, examples, and output templates. The delta in configuration time is often the difference between winning and losing a sale. Tie your outputs to the formats and KPIs that matter in that niche.

Build evaluations into your release process

Maintain smoke tests for the top five user tasks, plus negative tests for policy violations. Track pass rate, cost, and latency for each release candidate before you list it. When a marketplace asks for evidence, you should already have the graph.

Instrument everything

Emit structured logs for tool calls, decisions, and outcomes. Give buyers a basic analytics dashboard even if the host marketplace provides one. Your graph of runs by outcome becomes your renewal argument and a diagnostic when performance drifts.

Design handoff points

Define the exact step where a human reviews, approves, or edits. Add a preview stage that shows planned actions, data sources accessed, and the expected cost before execution. Human-in-the-loop is not a disclaimer. It is part of your product.

For teams focused on automation surfaces, the shift to the browser is especially relevant as browser-native agents overtake RPA. Marketplaces will favor agents that can operate across web tools with minimal setup.

What enterprises should do before year-end budgeting

Buyers also need a repeatable playbook. If you are planning your 2026 portfolio, use the next eight weeks to set guardrails and run targeted trials.

Define agent procurement categories

Treat agents as a distinct spend class with specific risk profiles. Write a one-page rubric that covers data access scopes, policy bindings, audit logging, version pinning, and rollback. Use it in every trial so teams do not reinvent the wheel.

Start with scoped pilots

Choose one process with measurable outcomes, like invoice matching or level one support triage. Time-box a four-week pilot and require two comparable agents to run side by side. Benchmark accuracy, median cost per case, and time to completion. Buy the winner and publish the scorecard internally.

Decide on your governance layer

If you live inside a platform like Salesforce, marketplaces such as AgentExchange fold into existing controls. If you are platform-agnostic, standardize on an agent gateway with policy enforcement, tool registries, and audit logs, then let teams try marketplaces on top. As you scale, you will likely formalize the function, echoing how Governed AgentOps goes mainstream.

Insist on cost transparency

Require creators or marketplaces to show price formation: model cost, tool cost, and creator margin. Ban pricing units buyers cannot forecast, and set default throttles per user or per department until you have reliable usage patterns. Establish exception paths before you need them.

Build an agent registry

Even if your first ten agents are bought, not built, catalog them with owners, data scopes, evaluation results, and renewal dates. When each agent has an owner, sprawl stays under control and knowledge accrues to the people running the work.

Tie success to a financial statement

Pick a cost line or revenue line that the agent should move. Examples: reduction in contractor spend for reconciliation, shorter cash conversion cycle from faster collections, or net promoter score lift from quicker case resolution. If it does not move a line on a statement, ask why you are running it.

How marketplaces will compete in 2026

The surface area is widening, but the win conditions are getting clearer.

• Catalog quality will beat catalog size. Winners will not just list more agents. They will list evaluated agents with predictable behavior, clean schemas, and stable interfaces. Expect minimum eval scores and periodic recertifications.
• Vertical depth will matter. Sector packs will include shared knowledge bases, canonical schemas, and reference policies. A healthcare claims pack that already maps to common provider formats will beat a generic automation agent every time.
• Agent operations will become a job. Just as DevOps and MLOps professionalized deployment and monitoring, AgentOps will professionalize policy tuning, tool registry management, and release management. Marketplaces will compete on built-in tools for these tasks and on how they surface risk.
• Pricing will converge on hybrid models. Flat per-run pricing is easy to buy, but some work requires variable cost. Expect a default hybrid of base price plus metered tool usage, with enterprise caps and out-of-policy failsafe modes.
• Identity and permissions will be a feature. The leading marketplaces will make it trivial to map agents to single sign-on, role based access control, and data loss prevention policies without custom work.

Risks to watch and how to mitigate them

No platform removes risk. The question is whether the platform helps you manage it intentionally.

• Prompt injection and tool chaining pitfalls. Require dry runs, policy simulation, and least-privilege tool scopes. Block unexpected data exfiltration with simple allow lists and per-tool approvals.
• Hidden model costs. For agents that call large models, costs can spike. Use budgets with alerts, and have agents report predicted cost per run before execution. Hold creators accountable for variance.
• Data residency and privacy drift. Demand data flow diagrams from marketplace hosts. Pin data processing regions and require per-agent declarations of what data leaves your boundary.
• Agent sprawl. Use the agent registry to enforce one owner per agent. Archive non-performers and cap departmental catalogs until usage justifies growth. Sunsetting is a feature, not a failure.

A practical checklist for week one

If you are a builder:

• Publish a minimal but complete vertical bundle with shared schemas.
• Add a visible change log and a rollback profile that buyers can pin.
• Ship an evaluation suite that measures task success, latency, and cost.
• Instrument structured logs and expose a basic, filterable dashboard.

If you are a buyer:

• Write a one-page rubric that every agent must clear before a trial.
• Pick two high-signal, low-risk processes and run side-by-side pilots.
• Establish budget caps, alert thresholds, and a per-user throttle.
• Create an owner-backed registry entry for every agent in flight.

The bottom line

Agent marketplaces are becoming the default distribution layer for LLM-powered work. MuleRun’s 2.0 shows what the startup path looks like: creator monetization, per-run pricing, clear versioning, and vertical bundles that make first value fast. Salesforce shows the enterprise path: governance, embedded controls, and deep workflow integration. GitHub points to a future where teams run multiple specialized agents under one roof with policy and metrics by default.

What happens next will look familiar. Catalogs expand, standards harden, tools mature, and procurement catches up. The winners will not be the marketplaces that shout the loudest. They will be the ones that make the shortest path from intent to outcome, with safety you can verify and economics you can defend.

If you build, ship an agent that respects that path. If you buy, write the one-page rubric and run the side-by-side pilot. The era of agent demos is ending. The era of agent distribution has begun.