Lessons
Two long-form lessons on the wider system: how the agentic economy works as a market, and how it will reshape jobs, roles, and organizations.
The Agentic Economy
The agentic economy is the shift from software people operate to software agents coordinate. If you build an agent that can transcribe audio, and someone uses your agent to turn audio support calls into text that can be analyzed for patterns, there’s a new economy that covers agent creators and the work of the agents themselves.
In the app economy, value was captured through interfaces. Companies built destinations, competed for attention, and tried to keep users inside their products. The user had to know which app to open, which buttons to click, and how to move information between systems.
In the agentic economy, value moves toward orchestration and outcomes. A user declares a goal. An agent breaks that goal into tasks, recruits the right tools or services, executes the workflow, and returns a result for review.
A CRM is no longer only a place a salesperson logs into. It becomes a set of structured actions an agent can call: update an opportunity, fetch account history, create a follow-up task, or identify renewal risk.
A travel site is no longer only a destination for search. It becomes a capability an agent can use while planning a trip, from booking the flight to ordering the right clothes to wear.
A legal service is no longer only a portal. It becomes a specialized agent or tool that can review a clause, check compliance, or draft a contract section.
The New User Journey
The old user journey looked like this:
- Open an app.
- Navigate the interface.
- Search for information.
- Copy data into another tool.
- Repeat the process across several systems.
- Assemble the final result manually.
The agentic journey is shorter:
- Declare the intent.
- Let the agent coordinate the work.
- Review the result.
- Approve, revise, or reject.
The human does not disappear. The human moves upstream, from operating software to defining goals, constraints, and judgment.
Orchestration Becomes the Control Plane
The most important layer in the agentic economy is orchestration: the system that decides which tools to call, which agents to involve, what context to pass, when to ask for approval, and how to verify the outcome.
This is why protocols matter.
Model Context Protocol (MCP) gives agents a common way to access tools, APIs, files, and data sources. Agent-to-agent patterns let specialized agents advertise what they can do and collaborate on larger goals.
Together, these patterns make software more composable. Even though building software is getting much easier with LLMs, there is still some overhead involved, and so exposing larger software packages and infrastructure as MCPs makes software even easier to build and operate. Instead of every company trying to build every feature, companies can specialize in being the best capability inside a larger network.
Business Models Change
The app economy favored subscriptions and per-seat pricing. That made sense when software value depended on how many humans logged in.
Agentic AI challenges that model. If one agent can perform work that once required many users, per-seat pricing no longer maps cleanly to value. The market moves toward pricing based on tasks, resolutions, usage, or outcomes.
Examples include:
- Paying for each support conversation resolved.
- Paying for each meeting booked.
- Paying for each document processed.
- Paying for each successful workflow completed.
This shifts risk. Customers prefer paying for results. Vendors must prove that their agents complete work reliably and profitably.
Discovery Changes Too
In the app economy, discovery was about human attention: search rankings, app stores, advertising, reviews, and brand recall.
In the agentic economy, discovery becomes algorithmic. If a user’s agent is choosing tools or vendors, the question becomes: can the agent understand your product, trust your data, and determine that you are the best option for the task?
That creates a new discipline: making products, services, and data agent-readable.
Structured information, clear APIs, trustworthy policies, transparent pricing, and machine-readable capabilities become strategic assets.
The Convergence Economy
As agents reduce the cost of searching, negotiating, coordinating, and transacting, the boundaries between firms, tools, and markets begin to change.
Small teams can rent capabilities on demand. Specialized providers can plug into larger workflows without owning the whole customer interface. Companies can form temporary software stacks for specific tasks and dissolve them when the work is done.
The result is a convergence economy: less defined by static software categories, and more defined by dynamic coordination around human intent.
The Strategic Takeaway
In the agentic economy, control beats interface.
The companies that win will not only build better dashboards. They will build trusted capabilities that agents can discover, call, verify, and pay for. They will make their data and services legible to machines while keeping human accountability where it matters.
The future of software is not just more automation. It is a new market structure where agents coordinate work across the web.
Agentic AI and Jobs
Agentic AI will not only change software. It will change how work is organized.
The central shift is from data consumption to workflow execution. For decades, digital transformation helped people see work more clearly through dashboards, reports, analytics tools, and centralized systems. Agentic AI goes further. It can act on the information those systems reveal.
That means many workers will move from doing every step of a task to supervising, designing, and improving workflows that agents help execute.
The End of Passive Dashboards
A dashboard tells a customer success manager that an account is at risk.
An agent can investigate why usage dropped, read recent support tickets, summarize the account history, draft an outreach email, create a CRM task, and recommend the next best action.
That does not remove human judgment. It changes where human judgment is applied. The person becomes responsible for defining the playbook, approving sensitive actions, handling exceptions, and improving the system over time.
Everyone Becomes Closer to a Builder
Agentic AI lowers the cost of creating software-like workflows.
A finance analyst may design an agent that reconciles invoices. A support lead may design an agent that classifies tickets and drafts responses. A legal operations manager may design an agent that reviews contract clauses before sending edge cases to counsel.
These people are not all becoming software engineers. They are becoming workflow architects: domain experts who can describe what good work looks like, identify exceptions, and guide agents with the right context and constraints.
The spreadsheet is a useful analogy. Spreadsheets did not turn every accountant into a programmer, but they did let domain experts build powerful models without waiting for engineering. Agentic AI can do something similar for operational workflows.
New Role Families
As agents become part of everyday work, new roles emerge around building, deploying, and supervising them.
Functional Agent Builders — domain experts in teams like Sales, Finance, HR, Legal, Support, and Operations. They understand the messy details of a workflow and can translate those details into agent instructions, examples, tools, and review criteria. They are valuable because the hardest part of automation is often not the model. It is knowing what should happen when the real world is ambiguous.
Forward-Deployed Engineers — they sit between product, engineering, and the business. They help turn promising AI demos into working systems inside real customer or internal environments. They connect models to data, integrate tools, debug edge cases, and make sure the agent actually produces measurable outcomes. Their success is measured less by lines of code and more by speed to operational impact.
Context Engineers — agents need more than raw data. They need meaning. Context engineers build the semantic layer that tells agents how to interpret business concepts, data fields, permissions, definitions, and policies. For example, an agent should know which revenue number is official, which customer field is deprecated, and which data source is allowed for a given workflow. Without context engineering, agents become brittle.
Agent Platform and Governance Leads — as agents gain tool access, companies need people responsible for permissions, observability, audit trails, rollback plans, cost controls, and safety policies. This is the AgentOps layer. It ensures that agents do not quietly accumulate too much power, leak sensitive information, or continue executing after a failure.
Agent Supervisors — they manage exception queues, review outputs, tune policies, and decide when a task should escalate to a human expert. As agents handle more routine work, supervision becomes a higher-leverage version of operations. The human role shifts from doing every task to maintaining quality across many agent-executed tasks.
Jobs Will Change Unevenly
Agentic AI will not affect every job in the same way.
Tasks that are repetitive, digital, well-documented, and reversible are the easiest to automate. Tasks that require high trust, human relationships, physical presence, deep accountability, or ambiguous judgment will change more slowly.
The key question is not “Will this job disappear?” A better question is “Which parts of this job can become agent-supported, and which parts become more important when routine execution is automated?”
The New Decision Rights
When an agent can take action, organizations need clearer decision rights.
An agent may be responsible for executing a task, but a human or team remains accountable for the outcome. That distinction matters. If an agent sends a customer email, approves a refund, updates a forecast, or changes production software, the organization still needs ownership, logging, review, and escalation paths.
Good agentic systems make accountability visible.
The Career Opportunity
The biggest opportunity is for people who combine domain judgment with agent fluency.
Valuable workers will know how to:
- Describe workflows precisely.
- Identify which steps should be automated, supervised, or kept human.
- Provide the context agents need.
- Evaluate outputs.
- Improve prompts, tools, and policies over time.
- Communicate risk clearly.
Agentic AI does not make human expertise irrelevant. It makes expertise more important, because agents need good goals, clean context, and strong supervision to create value.
The future of work is not everyone becoming a coder. It is more people becoming designers of how work gets done.