Contents
- Token and agents are bringing several developments into one new bargaining deal
- Graph engineering and cloud agents are forcing marketing teams to work differently
- Token costs and long workflows are changing who holds bargaining power
- The sponsored token pipe is still a prediction, not an AI ad standard
- Vietnamese agencies need to judge AI agents by cost and output
- Testing thresholds to set before scaling AI agent budgets
- References
AI agent and automation are reshaping where bargaining power is created in the marketing tools market. The advantage is no longer just about which model is smarter, but about who controls the workflow, data, running costs, and the path to results.
For Vietnamese marketers, this is a real shift in how SaaS is chosen, agencies are hired, and budgets are allocated: buying a tool may no longer be enough; you need to know how much work it can do, at what total cost, and who holds approval rights over the output.
Key points
- AI agents are shifting competition from features to workflows that can run long, run in parallel, and be measured.
- Token costs and ad-funded models can affect the results users see.
- Teams that can connect agents to cloud, async workers, data, and distribution channels will have stronger buying power.
- Vietnamese marketers should test with small tasks, limit publishing rights, and measure cost per output.
Token and agents are bringing several developments into one new bargaining deal
A prediction on X by Antonio García Martínez suggests that having a layer that routes AI requests through different processing paths could lead to two payment models: full price, or a lower price with commercially visible sponsored content. He also imagines a revenue-sharing system that measures an app’s contribution when an agent sends users to a merchant. This is a personal forecast, not a confirmed product announcement.
At the other end of the value chain, Google’s graph engineering learning material describes how to move from a single agent to a system that can run continuously and improve itself. Railway also explains how to move coding agents to remote machines to run long tasks, connect through CLI, and coordinate multiple agents. Kleo’s hiring post, meanwhile, asks for async workers, MCP, CLI, and WhatsApp or Telegram integration. Together, these examples point to one thing: the value lies in the operating system around the agent, not just in the chat window.
Graph engineering and cloud agents are forcing marketing teams to work differently
This section brings together what has been described concretely in the sources, from learning materials and runtime environments to hiring requirements. They show that marketing work will need to move closer to process design and system control.
Graph engineering means marketing teams must design agents around workflows
Graph engineering is a way to describe steps, states, and transitions between multiple agents or tools. The course introduced by Lunar moves from graph concepts to building the first agent and then to a self-improving graph. For marketing, this fits chains such as receiving a brief, generating variants, checking brand safety, requesting approval, and then sending content to publishing channels. Teams are no longer asking only whether the model writes well; they must define which step the machine can do on its own and which step still requires human approval.
Cloud coding agents free long tasks from the personal machine
Railway describes cloud agents that can run on remote machines, use CLI, connect with Codex desktop, keep working when the user is away from the computer, and coordinate multiple agents. Combined with graph engineering, this opens the door to workflows that run on a schedule or in parallel. Marketers now need to factor in machine costs, logs, access rights, and stopping points instead of only comparing subscription prices.
Async workers and MCP are becoming the new skill set for marketing tool builders
In Kleo’s job description, async workers, MCP, CLI, cloud, and WhatsApp or Telegram integration are listed alongside full-stack experience. That shows an agent tool that works in marketing needs to connect multiple services and handle asynchronous work. Buyers should therefore ask vendors about APIs, logs, task limits, and recovery capabilities, rather than looking only at the AI feature list.
Agentic schedulers are pushing growth from features to outputs
The analysis of Postiz describes a scheduler that pushes one asset to more than 30 channels, with recurring revenue reported at 186.713 USD per month. The story also highlights how one idea can be sold through distribution and reused across video, articles, clips, and engagement. For marketers, an agent is more valuable when it can connect an asset to reach and response, rather than just create another draft.
Token costs and long workflows are changing who holds bargaining power
Per-run costs can turn AI choice into a revenue decision
In the prediction about a “sponsored” token pipe, Antonio García Martínez argues that consumer apps could choose subsidized processing to reduce compute costs, while commercial systems pay through sponsorship or revenue sharing. Although this is not a confirmed model, the argument points to a marketing risk: AI answers may be influenced by how someone pays for the run.
The Postiz analysis offers the counterpoint. The tool does not just sell a posting schedule; it ties an agent to pushing one asset to more than 30 channels and creating demand through content. When output is measured by distribution, leads, or revenue, buyers have a basis to compare the real total cost against the result, rather than only comparing token prices.
Long-running workflows increase the value of whoever controls the system
Google’s graph engineering points toward continuously running systems, while Railway’s cloud agents target long tasks and multiple agents. Kleo’s hiring post adds requirements for async workers, MCP, and messaging-channel integration. Together, these three examples show that buyers are no longer choosing only a model; they are choosing the ability to connect a model to a stateful workflow, a schedule, and error handling.
That is why bargaining power is moving toward the team that can benchmark by task, track logs, and replace one component without rebuilding the entire workflow. Vendors may keep customers through operational data and deep integrations, but technically capable buyers will be less locked in.
User data and approval rights are becoming conditions of value
The prediction about ads in AI results emphasizes apps that keep their relationship with users and data in a GDPR-safe way. The MCP, CLI, and integration requirements in Kleo’s role show that agents must also work inside systems with specific access rights. Together with the multi-agent cloud capability described by Railway, this is why approval rights, audit logs, and data scope need to be included in purchase terms.
The sponsored token pipe is still a prediction, not an AI ad standard
Could ads inside AI answers distort buying choices?
A personal prediction by Antonio García Martínez is circulating that commercial AI results could include sponsored sections, revenue sharing, and agent attention auctions. There is no confirmation from any specific platform in this source, so it cannot be treated as a product, a measurement standard, or an advertising model already operating at scale.
What is actionable: marketing teams should prepare ways to identify sponsored content, keep track of referral sources, and separate organic CTR from interactions driven by incentives. When testing commercial AI tools, ask clearly how prompt data is used, whether results are influenced by sponsorship, and who is responsible when an agent recommends a choice.
Vietnamese agencies need to judge AI agents by cost and output
In Vietnam, many marketing teams will encounter agents through SaaS, agencies, or outsourced technical teams. The sources on Postiz and cloud agents suggest a more practical buying approach: start with one output that needs improvement, such as turning one asset into multiple versions and pushing it to multiple channels; only then choose the tool.
Agencies also need to price by task and by level of control. A cheap package with no logs, no publishing limits, or no separation of client data can create a higher real total cost once errors appear. For WhatsApp, Telegram, and social channels, recovery capability, human approval, and change history should be checked before letting an agent run automatically.
Testing thresholds to set before scaling AI agent budgets
- Choose one workflow with a clear output, and measure assets, leads, or distribution counts instead of the number of chats with the agent.
- Calculate the cost per task, including token, cloud, integration, log storage, and reviewer time.
- Limit the data the agent can read and what it can do on its own; lock publishing or spending steps after testing.
- Set scaling conditions: the output must hit the benchmark, have auditable logs, and include a fallback plan when the agent fails.
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References
- Antonio García Martínez — prediction about a routing intermediation layer and sponsored token pipe
- Lunar — introduction to Google’s Graph Engineering course
- Cam Trew — Full Stack Developer hiring requirements at Kleo
- Railway — Moving your Coding Agents to the Cloud
- NO1ennn — Postiz growth analysis
