What will agentic AI change? — From how we work to how industries earn profits

1. What is agentic AI?

Agentic AI refers to AI systems that select and use tools to pursue a goal, then check the results to determine their next actions. They carry out multiple steps, finding additional information or revising their work as needed. [1]

The process can be understood as: identify the goal → plan → use tools → check the results → continue or finish. Systems connect tools such as search, calculation, and document editing, with the specific setup varying by system. [2]

Crucially, agentic AI is not the name of one particular model. We need to examine the complete setup connecting a model with tools, task state, permissions, and verification procedures. Nor does “autonomous” mean unlimited authority or human-level judgment.

2. How does it differ from earlier AI?

This table compares capabilities. Actual products can combine generative AI, fixed procedures, and agents. [2]

When evaluating a product, look beyond its chat interface or scheduling features and examine what actions it can choose in response to the situation.

3. Who is building it? — As of October 10, 2026

Sources checked: Saturday, 2026-10-10, 22:24 KST · Official announcements, product documentation and current help pages

To understand today's competitive landscape, start with Meta Muse and OpenAI's ChatGPT dots. Both companies have introduced personal agents that carry goals and work forward between conversations. Google, Anthropic, Microsoft and Perplexity connect these capabilities to their respective work environments, while Manus, Cursor, Devin and Agentforce offer products for general tasks or specific kinds of work.

The following comparison covers major products. We checked the providers' feature descriptions; this is not a performance ranking based on testing identical tasks. Distinguish current availability, phased rollout, private preview and future plans when reading these descriptions.

3-1. Meta — Muse: From personal assistance to small-business operations

Muse is a personal agent designed to carry out work. It uses a dedicated cloud computer and browser to handle multiple steps, including email, travel bookings and web forms, and is designed to keep working after the app closes. Users communicate through the Muse app and WhatsApp. Meta says sensitive actions such as sending email or making purchases require user approval. Muse Spark is the name of the underlying model and should be distinguished from the Muse agent service. [3]

The recent expansion is into business operations. Muse for Small Business connects Instagram professional-account analytics, Facebook Pages and Meta ad accounts with work tools such as Shopify, Canva, QuickBooks, Slack and Notion. Official examples include analyzing sales and campaign data to prepare growth plans, drafting upcoming advertisements and identifying unusual expenses. Publishing, sending and spending require approval. [8]

Current access conditions: A follow-up official announcement confirms availability in the US and Canada. There is free usage and additional subscription access; the official sources we checked do not specify general availability in South Korea. We prioritized the later regional information over the US-only description in the original September 8 launch document. [8]

Industry interpretation: Meta is connecting advertising, storefront and customer-interaction data to operational work. This can be read as a strategy to expand into handling small-business tasks. Feature expansion alone, however, does not establish Muse's revenue or profitability.

3-2. OpenAI — ChatGPT dots: Work that continues between conversations

The official product name is dots; a user's individual agent is a dot. Powered by GPT-6 Astra, it uses its own cloud computer and browser alongside connected apps. Given a goal, it carries progress across conversations and tasks, then handles next steps as information and feedback change. Official examples include refreshing research or identifying fixes from customer feedback and preparing code, tests and proposed changes for review. [31]

Its relationship to ChatGPT Work and Codex also matters. A dot is the agent that keeps track of an ongoing responsibility; Work and Codex are work environments it can use to carry out tasks. Official help explains that a dot can create Work or Codex tasks on a connected local computer. Listing the three names as though they were the same product obscures their roles. [32]

Current access conditions: Pro access is rolling out in eligible markets excluding the EEA, Switzerland and the UK; Business Premium access is rolling out in supported ChatGPT regions. Enterprise, Edu and Healthcare access is a beta requiring administrator activation. The first dot is included in Pro and Business Premium, but deeper work has usage limits. Permissions and approval rules for connected apps and computers determine what it can execute. [32]

Industry interpretation: The product expands delegation from issuing a new instruction for each task to assigning ongoing responsibility. Accumulated work context and connections may increase its value over time, but actual time savings and review effort need to be assessed for each workflow.

3-3. Where do other major general-purpose and work agents stand?

Do not confuse these three names. At Meta, Muse is the agent product and Muse Spark is the model. Google's Gemini Spark is a separate personal agent, with different product and access conditions from the enterprise Gemini agent announced on October 8. [3] [5] [6]

3-4. Products for specific work such as coding and customer support

On September 16, Salesforce also announced AIforce. This layer exposes Salesforce data and business rules through other interfaces such as Claude and Slack. The launch announcement describes Salesforce in Claude as a beta. Its role differs from Agentforce's role-specific agents. [35]

3-5. Platforms for companies building their own agents

The central competitive question is who can take on more of the work. Companies connecting personal apps and memory, companies holding organizational documents and customer data, and companies turning task execution and review into products are competing from different starting points. This interpretation follows from the product configurations above; it is not a finding of market-share or performance superiority.

4. How can companies and individuals use it?

Individuals can start with ready-to-use products, while companies can connect data, rules and permissions for recurring work. The following are usage scenarios based on verified capabilities, not success stories or productivity measurements from our own testing.

InveDeck readers could expand a request from "Tell me this quarter's revenue" to "Check the filings, align accounting definitions for a comparison with the previous quarter, and explain the changes in cash flow." Work products can prepare the report, but quarterly versus cumulative figures and FCF calculations still need checking. This is an example of using external agents; it does not mean InveDeck currently offers a command-execution feature.

Claims that work continues after closing an app depend on the execution environment. Cloud tasks may continue, but some products require the device or desktop app to remain on when using files or a browser on your computer. Pending approvals, expired logins and exhausted usage allowances can also pause work. [10] [11] [13] [15]

5. How might the near term differ from the more distant future?

Over the next 1–2 years, adoption may broaden in tasks with clear boundaries: handling one customer inquiry, producing a report from specified materials, or testing a code change. These tasks allow explicit completion criteria. People may shift time from performing every step themselves toward defining goals, exceptions, and approval requirements, then reviewing the results.

This does not immediately imply the disappearance of entire occupations. A job combines repetitive tasks, negotiation, on-the-ground judgment, and decisions carrying responsibility. We need to distinguish tasks that shrink from those whose demand grows. Even with rapid usage growth, integration and supervision costs may delay labor-cost savings.

In the more distant period, 3 years and beyond, agents from different companies may coordinate schedules, inventory, and orders, and take over a broader range of long-running work. In this scenario, data and transaction rules that agents can read and execute accurately become important alongside web interfaces designed for people.

Achieving this requires addressing reliability over long tasks, identity and permissions, dispute resolution and accountability, and costs. The existence of connection standards alone does not make it safe to entrust intercompany transactions to agents. Current evidence cannot establish when fully unmanned companies might become widespread.

6. Where might the technology develop?

Advances in reasoning and multimodal capabilities provide the foundation. They combine the ability to break down complex goals and revise plans based on results with the ability to read tables, charts, and screens as well as text. OpenAI's September 2026 GPT-6.1 Sol announcement also evaluates tasks involving multiple tools and comprehension of PDFs and charts. However, the company's performance evaluations should not be read directly as success rates in real-world work. [23]

First is the ability to preserve task state accurately over long periods. Reading a long document at once differs from correctly remembering important decisions several days later. Anthropic describes conversation compaction, structured notes stored outside the conversation, and separate subagents as approaches to long-running tasks. Designers must decide what to store, update, and forget. [24]

Second is connection to real execution environments. APIs let programs exchange data; computer-use capabilities read and operate interfaces. Interface operation can reach legacy systems without APIs, but requires procedures to check for interface changes and incorrect inputs. [25]

Third is interoperability. MCP focuses on connecting agents to tools and data, while A2A focuses on communication and coordination between different agents. Such standards can reduce the effort of building each connection from scratch, but do not themselves verify data accuracy or whether an action is legitimate. [26]

Fourth is collaboration and verification. Multiple agents can broaden coverage by dividing independent research. However, Anthropic's 2026 multi-agent research also observed coordination failures in projects with interdependent work and repeated similar errors. Adding more agents does not automatically improve quality. [27]

Taken together, these developments suggest that competition will involve not only longer answers but also the ability to verify execution results, detect failures, and stop appropriately. This is an interpretation that execution environments, permission management, and evaluation techniques must advance alongside model capabilities.

7. How might industries be affected?

The following industry analysis is inferred from the technological and product developments above. It does not establish beneficiaries or predict share-price increases. Even within an industry, outcomes may differ according to customer data, sales channels, operating costs, and investment requirements.

Viewing CPUs and GPUs solely as substitutes misses part of the picture. NVIDIA's 2026 technical explanation describes a recurring cycle connecting GPU model inference with CPU execution of code and tools, data processing, and task coordination. If either side waits too long, overall throughput can slow. This does not establish a universal CPU-to-GPU ratio for all agent systems; the ratio depends on the workload and configuration. [28]

Looking only at the direction of power-demand growth is also insufficient. The IEA's 2026 report examines grid, equipment, and financing constraints on data centers together. It concerns data centers as a whole and should not be read as demand attributable solely to agentic AI. Investors should check whether announced construction plans translate into actual operations, revenue, and cash recovery. [29]

8. What to check before granting more authority

Muse says a separate Sentinel reviews outgoing requests. The current Secure VM differs from the planned Confidential VM intended to cryptographically prevent Meta itself from accessing data. The statement that conversations and VM data are not shared with advertising systems must also be distinguished from the conditions permitting model training. Alongside tool permissions, check training opt-out settings, organizational contracts and data-retention terms. [21]

Agent errors can extend beyond text into file changes or incorrect business actions. Systems can also mistake instructions hidden in external webpages or documents for legitimate task instructions. Anthropic describes isolated execution environments, file and network boundaries, and granular tool permissions as ways to reduce these and other risks. A single approval dialog cannot address every risk. [30]

A practical rollout starts by defining success, permitted actions, and conditions for handing work to a person. Teams can begin with reading and drafting, observe results, and separately design permissions and checks for consequential actions such as sending, paying, and deleting. Execution records and ways to reverse actions are also part of the workflow.

9. The metric investors should watch: Cost per completed task

Growth in users or tool calls alone does not establish improved business profitability. Long tasks involve not only model calls but also search, code execution, storage, monitoring, retries, and human review. InveDeck proposes the following comparison measure.

Total cost per completed task = (The period's combined model, execution-environment, search, storage, security, human-review, and rework costs + Allocated implementation costs) ÷ Number of completed tasks that pass review

A low-priced model that fails frequently can increase the final cost of completing work. A more expensive model that reduces rework can lower total cost. Before-and-after comparisons should hold task difficulty and quality standards consistent, and distinguish time saved from actual cost reductions or additional revenue.

For cloud and semiconductor companies, also assess whether this demand translates into revenue and margins, and whether operating cash flow covers increased investment and working-capital needs. Good technology, products that sell well, businesses that generate cash, and attractive share prices each require separate assessment.

Related reading · NVIDIA and AMD: Growth and cash flow

Related reading · Big Tech's AI investment and cash burden

Related reading · How Oracle's contracts become revenue and cash

10. Four questions to ask when reading future news

1. What work does the system actually complete, and where do people intervene?
2. Is it a demonstration, beta, or production deployment, and who measured the published results?
3. Has cost per completed task fallen after including errors, rework, and review?
4. Can the effect be observed in customer retention, profit, and cash flow?

Agentic AI is expanding the scope of work we can delegate. The scale of that change is demonstrated by what work systems repeatedly complete, how accurately, and at what cost—not simply by calling them agents.

Sources

1. IBM · What is agentic AI? — Concepts and goal-driven execution.
2. Anthropic · Building effective agents — 2024-12-19. Distinguishing workflows from agents and explaining execution structures.
3. Meta · Introducing Muse — Announced 2026-09-08; page updated 09-30. Personal agent, VM, WhatsApp and persistent execution.
4. OpenAI · Meet dots — Current product documentation. A dot's role and plan, regional and administrator conditions.
5. Google · Gemini Spark — Current product guide. Tasks, Skills, Schedules and app connections.
6. Google Cloud · Introducing the Gemini agent — 2026-10-08. Enterprise agent, multiple models and management architecture.
7. Anthropic · Claude Cowork and chat are now one Claude — 2026-09-16. Integration approach and phased rollout.
8. Meta · Muse for Small Business — Announced 2026-09-29; page updated 10-08. Business connections and US/Canada availability.
9. OpenAI · Tasks and memory — Current help. Task coordination, schedules, events and memory.
10. OpenAI · Connect computers and apps to your dot — Current help. Cloud and local execution, logins and plugins.
11. Google · Use Gemini Spark to manage your tasks & workflows — Current help. Accounts, execution environments, approvals and limits.
12. Google Korea · Introducing Gemini Spark, a 24/7 personal AI agent — Announcement of Korean-language availability in South Korea. The body does not specify an exact publication date.
13. Anthropic · Get started with Claude Cowork — Current help. Cloud and local requirements, memory and paid plans.
14. Microsoft · Introducing the new Copilot with Home, Code and Autopilot — 2026-09-25. Enterprise product strategy and preview status.
15. Perplexity · What is Computer? — Updated 2026-09-29. Cloud task execution, connections, scheduling, subscriptions and credit conditions.
16. Cursor · Cloud Agents — Current product guide. Independent execution environments, code changes, testing and change review.
17. Cursor · Cloud Agents and Cursor Harness Improvements — 2026-08-19. Event-driven and scheduled execution, PR follow-up and subagents.
18. Salesforce · Expansion of role-specific Agentforce agents — 2026-09-14. General availability for Casey, Paige and others; Hunter's separate release status.
19. AWS · Amazon Bedrock AgentCore — Current product guide. Model and framework integration, deployment, authentication, observability and operation.
20. Microsoft Learn · Agent flows overview — Updated 2026-08-03. Manual, event-driven and scheduled execution, connectors and approval steps.
21. Meta · How We Built Safety Into Muse — 2026-09-08. Sentinel, approvals, VM distinctions and data use.
22. OpenAI · ChatGPT Work — Current product information. Files, tools, and recurring tasks.
23. OpenAI · Introducing GPT-6.1 Sol — 2026-09-29. Tool-use and multimodal performance evaluations.
24. Anthropic · Effective context engineering for AI agents — 2025-09-29.
25. Microsoft Learn · Automate web and desktop apps with computer use — Current developer documentation. Interface-based execution.
26. Linux Foundation · A2A adoption in its first year — 2026-04-09. The roles of A2A and MCP.
27. Anthropic · Patterns and problems in emerging multiagent systems — 2026-08-13.
28. NVIDIA · Vera CPU and Agentic Workloads — 2026-05-31. CPU execution and GPU inference.
29. IEA · Key Questions on Energy and AI — 2026-04-16. Data-center energy and supply constraints.
30. Anthropic · How we contain Claude across products — 2026-05-25. Isolation, permissions, and external inputs.
31. OpenAI · Introducing dots — 2026-09-29. Official launch announcement, always-on work, cloud computers and supported plans.
32. OpenAI Help Center · Getting started with your dot — Accessed 2026-10-10. Plan, regional and administrator conditions; connections to Work and Codex; usage limits.
33. Manus · Download / Product guide — Accessed 2026-10-10. App availability, tool connections, and website, app and slide creation.
34. Cognition · Devin — Accessed 2026-10-10. Code, browser testing, cloud and local execution, and work integrations.
35. Salesforce · AIforce announcement — 2026-09-16. A connection layer for external AI interfaces; Salesforce in Claude beta.