1. Introduction: AI moves from answering to acting
The first big wave of AI gave us conversational machines. They wrote emails, summarized reports, brainstormed campaigns and helped with code. That was a big change.
But AI agents take the idea further. An agent does not just answer a question. It can be given a goal, plan how to proceed, use tools, gather information, work through intermediate steps and come back with a result. In that sense, an agent resembles a search engine less and a digital employee more.
Google defines AI agents as software systems that use AI to pursue goals and complete tasks on behalf of users. They involve reasoning, planning, memory and a degree of autonomy.[1]
This sounds technical, but the impact is very practical. When an agent can read emails, look up background information, update the CRM system, run an Excel analysis and draft a reply to a customer, the structure of work changes. People no longer make every click themselves. They set the direction, check the quality and take responsibility.
That is why AI agents are not just a new software category. They are a new way of organizing work.
2. What is an AI agent?
An AI agent is an AI program that can act relatively autonomously to achieve a goal. Put simply:
AI agent = AI + a goal + tools + the ability to take multiple steps.
A regular chatbot responds to: “Write me a sales email.”
An AI agent can do this: “Find 20 potential customers, check their websites, assess their fit, log the details in a spreadsheet and draft a personalized email for each one.”
The difference is big. An agent can use, for example:
- a browser to search for information
- email to write messages
- a calendar to book time
- a database to check customer information
- a code editor to build software
- the company’s internal documentation to find answers
- automation tools to trigger tasks
Anthropic makes a useful distinction between two things: workflows and agents. In a workflow, AI moves through a predefined process. An agent, on the other hand, can decide for itself what steps are needed to reach the goal.[2]
This distinction matters. If AI always runs the same form-filling process, it is essentially automation. If it can assess the situation, choose a tool, revise its plan and keep going with the task, it is closer to a genuinely agentic system.
3. How is an agent different from a chatbot?
A chatbot is a conversation partner. An agent is an actor.
The logic of a chatbot is often: 1) A human asks. 2) The AI answers. 3) The human takes the next step.
The logic of an agent is: 1) A human sets a goal. 2) The agent breaks the task into parts. 3) The agent uses the tools it needs. 4) The agent works through intermediate steps. 5) The agent returns a result or asks for approval at a critical point.
This changes the human role. People become less like typists and more like directors of the work. They do not necessarily write every paragraph of a report, but they define why the report is being made, who it is for and what counts as a good enough result.
This echoes a classic question in startup leadership: what should be centralized and what should be handed to autonomous teams? In a Startup-ministeriö podcast discussion of Supercell’s thinking, the idea that stands out is that decisions are best made as close to the product and the customer as possible, provided the organization is built for it.
With AI agents, the same principle takes on a new form: what should you let the agent do close to the task itself, and where must a human keep hold of the reins?
4. Why are AI agents on the rise right now?
AI agents are not an entirely new idea. Agents have been discussed in AI research for a long time. What is new is that large language models have made them practical.
A language model, a large AI model, can understand and generate language. When you combine it with tools, memory and operating instructions, it can do much more than write text.
At least five factors are behind the rise.
1. Models have improved
New AI models can handle longer instructions, interpret context and perform multistep reasoning better than earlier versions. They are not infallible, but they are good enough for many well-defined work tasks.
2. Tool use has matured
An agent can call external tools: retrieve information, use APIs, send messages or execute code. An API is an interface through which programs can talk to each other. When an agent uses an API, it does not just “think up” an answer — it can actually do something in a system.
3. Companies are looking for productivity gains
According to McKinsey's 2025 global survey on AI, 23 percent of respondents said their organization was scaling an agentic AI system in at least one business function, and 39 percent were experimenting with them.[3] This tells us two things: interest is widespread, but maturity varies a lot.
4. Work is full of recurring but not fully mechanical tasks
Emails, customer reports, research, data cleanup, draft proposals, internal memos and basic code scaffolding are tasks that require flexibility. They are not so simple that traditional automation always works well. But they do not always require deep human judgment from start to finish either. This in-between space is a gold mine for agents.
5. Competition is intensifying
Companies face the same question as in many technological upheavals: who will be first to learn how to use the new tool sensibly? Not who adopts the most AI, but who builds a working way of doing work around it.
5. The technical foundation: model, tools, memory and goals
An AI agent usually needs four basic components.
1. The AI model
This is the agent’s “brain.” The model interprets instructions, draws conclusions and generates responses.
2. Tools
An agent can use, for example, a browser, a calculator, a database, email, a calendar, a code editor or company systems. Without tools, an agent is little more than a conversationalist. With tools, it becomes an actor.
3. Memory
Memory can mean two things. Short-term memory helps the agent stay within the context of the task at hand. Long-term memory can contain information about the user’s preferences, company guidelines or past decisions. Memory makes an agent more useful, but also riskier. If an agent remembers the wrong things or handles sensitive information carelessly, the problem grows quickly.
4. The goal and the boundaries
A good agent needs a clear goal.
A poor goal: “Handle the marketing.”
A better goal: “Draft next week’s LinkedIn content plan for a B2B software company. The target audience is CFOs. Use three themes: cost savings, automation and a customer story. Don’t publish anything without approval.”
An agent also needs to be told its boundaries. What is it allowed to do on its own? What can it propose but not carry out? At what point does a human need to approve?
OpenAI’s practical guide to building agents stresses that high-risk actions, such as payments, large refunds or order cancellations, should remain under human oversight until the system’s reliability is sufficiently well understood.[4]
6. Practical applications at work
The first useful applications of AI agents are not necessarily dramatic. They are boring. And that is exactly why they are valuable.
Research and background work
An agent can look up information about the market, competitors, customers or regulation. For example, an entrepreneur might ask: “Find five competitors in the Swedish market, summarize their pricing, customer promise and differentiators. Finally, recommend where we should position ourselves.” Here the agent does not replace strategy — it does the background work faster.
Email and customer communication
An agent can draft replies, track open customer conversations and remind you about missing follow-ups. Good use is not: “Let the agent send all messages automatically.” Good use is: “Let the agent prepare drafts and surface urgent matters.”
Writing code
In software development, agents are already well advanced. They can write tests, suggest fixes, build prototypes and explain legacy code. This does not make developers redundant. It shifts the focus. The developer becomes more of an architect, a reviewer and someone who defines the problem.
Sales and CRM
CRM stands for customer relationship management system. It is where a company records its sales pipeline, customers and communications. An agent can update the CRM, pull background information on a customer, suggest the next message and prioritize leads. But it can also do damage if it logs incorrect information or sends generic messages to the wrong person.
Recruitment and HR
An agent can help draft job postings, handle candidate communication and put together an interview framework. This is where risks rise quickly. Recruitment is about people, equal treatment and decisions that have a major impact on lives. The agent must be an assistant, not an automatic gatekeeper.
Leadership and decision preparation
An agent can compile a situational overview for the executive team: sales, customer feedback, cash flow, recruitment, risks. The scaleup discussions on the Startup-ministeriö podcast highlight the idea of a management system as a whole: people, processes and tools must serve the desired outcome, not just keep meetings running. AI agents fit this well if they are tied to goals. They fit poorly if they are adopted only because “everyone else has agents.”
7. The business perspective: efficiency or a new leadership challenge?
For companies, an AI agent is a tempting promise: more done with less friction. But the real benefit does not come from buying an agent tool. It comes from redesigning work.
A company should ask:
- Which tasks are recurring but require some judgment?
- Where does information move slowly?
- Where do people copy the same data from one system to another?
- Where are mistakes expensive?
- Where does speed deliver a real competitive advantage?
- What must not be automated?
Agents force companies to look honestly at their processes. If a process is bad, an agent can make it bad faster. If responsibilities are unclear, an agent can add to the confusion. If the data is messy, an agent can produce convincing but wrong conclusions.
This is where leadership really comes in. An AI agent is not just an IT project. It is a change in the operating model.
8. The opportunity for startups and growth companies
For a startup, an AI agent can be an unfair advantage. A small team can look bigger than it is. A founder can handle sales research, marketing, customer support, documentation and code prototyping with the help of agents.
But there is also a trap here. A startup’s most important job is not to automate everything. Its most important job is to find the right customer problem and build a solution someone will pay for.
Discussions on the Startup-ministeriö podcast strongly emphasize focus: the best founders spend their energy on customers, the product, the team and growing revenue, not on all the surrounding noise.
This is exactly where an AI agent can help, if it frees up time for what matters.
Good startup use:
- the agent finds customer lists
- the agent summarizes customer interviews
- the agent maps the competition
- the agent drafts a landing page
- the agent builds an MVP prototype
MVP stands for minimum viable product, the smallest working version of a product that can be used to test whether the solution interests real customers.
Poor startup use:
- the agent churns out endless content without a strategy
- the agent sends mass emails to the wrong audience
- the agent builds features nobody asked for
- the agent gives the founder a feeling of progress even though the business is not moving forward
Agents can make doing things easy. That is why you need to be even more precise about what is worth doing.
9. The individual perspective: a new coworker or a personal assistant?
For an individual, an AI agent can be a huge relief. It can help a student structure course material, an expert build a report template, an entrepreneur respond to a request for proposal and an executive prepare for a board meeting.
But the greatest benefit does not come from outsourcing your thinking to the agent. It comes from outsourcing the friction.
Finding information, formatting, the first draft, comparing options, building a checklist, scaffolding code — these are tasks where an agent can excel.
Helsingin Sanomat’s coverage of AI in everyday life shows the same duality: AI can help with things like workout programs and meal planning, but users need to be able to judge the training load, give feedback and question the results.
This is the basic lesson of AI agents for individuals:
Don’t treat an agent as an authority. Treat it as a fast helper that needs direction.
10. Risks, errors and accountability
AI agents are interesting precisely because they can do things. For the same reason, they are risky.
1. Hallucination
AI can make up incorrect information. This is a familiar problem from chatbots, but with agents the risk grows because the error can turn into action. In Helsingin Sanomat’s everyday AI experiment, the AI made up a name for a plant that does not exist and later admitted the mistake. The piece captures an essential warning: AI can be useful, but important things must be checked against reliable sources.
2. The wrong kind of autonomy
Not everything should be left for the agent to do alone.
A low-risk task: “Summarize these notes.”
A medium-risk task: “Draft a reply to the customer.”
A high-risk task: “Send the customer a refund decision and update the contract terms.”
The greater the impact on money, reputation, safety, health or rights, the stronger the human approval required.
3. Security
An agent with access to email, the calendar, customer data and the payment system is a powerful actor. If its access rights are too broad, the damage can be significant. Companies need to think about agents as seriously as they think about employee access rights: what is this actor allowed to see, change, send and delete?
4. Accountability
If an agent makes a mistake, who is responsible? The employee? The manager? The company? The software vendor? The model developer? This question is not yet settled everywhere. The OECD's report on agentic AI emphasizes that AI agents are capable of autonomous decision-making and action with limited human oversight, which raises the importance of governance and accountability.[5]
5. The illusion of quality
An agent can produce a neat report, a convincing table and a well-formatted email. The form can be fine even when the content is wrong. This may be the most dangerous risk in expert work: bad work looks good.
11. The ethical and societal perspective
AI agents do not just affect individual tasks. They affect power, skills and the division of labor.
Who gets the productivity gains?
If agents raise productivity, the benefit can go to employees, companies, customers or owners. The fair question is: does pointless work shrink, or does the pace simply intensify?
Whose work gets automated?
Agents can help experts, but they can also reduce the need for certain junior tasks. There is a contradiction here. Many people learn on the job by doing exactly the tasks an agent can now handle: research, reporting, drafting, analysis. If these disappear entirely, where will the next generation of experts learn?
How do we ensure transparency?
Customers should know when a decision was made by a human and when AI was involved. This is especially important in healthcare, finance, recruitment, education and public services.
How do we avoid digital inequality?
If some people and companies learn to use agents effectively and others do not, productivity gaps can widen. That is why AI literacy is no longer just a matter for tech enthusiasts. It is a workplace skill.
12. The future: hype, realism and the next directions
There is a lot of hype around AI agents. Not all work will be automated next year. Not every company will get a productivity leap just by adopting a new tool. But it would be a mistake to dismiss agents as a mere bubble. Realistic development will likely look like this.
1. Agents arrive first in scoped tasks
The first working agents are not general “do everything” systems. They are scoped helpers for employees: a sales lead enricher, a customer support reply drafter, a software test writer, a financial report preparer, an HR process reminder, an internal knowledge finder.
2. Human oversight will remain for a long time
Fully autonomous agents sound impressive, but in companies trust is built slowly. First the agent proposes. Then it handles low-risk tasks. Only later does it get more permissions.
3. Tools merge into existing systems
Agents will not necessarily appear as separate applications. They will show up in email, office software, CRM, project management, code editors and financial management.
4. Leadership changes
Leaders need to learn to lead not only people but also pairs made up of a human and an agent. This requires new questions: What does the agent do? Who checks? How is quality measured? When is the agent stopped? How do we learn from mistakes?
5. Trust and governance decide the winners
Technology alone is not enough. The winners will be the organizations that combine three things:
- good enough tools
- clear ground rules
- a bold but critical culture of experimentation
In Startup-ministeriö discussions, developing culture emerges as a key lesson: as a company grows and the market changes, its ways of working must evolve too. The same applies to agents. A company cannot just add AI to its old model and expect a miracle. The work itself has to change.
13. Conclusions
An AI agent is the next step in the evolution of AI because it shifts AI from producing text to carrying out tasks. That does not mean people will disappear from work. It means the human role will change.
Less manual shuffling. More defining of goals. Less empty routine. More evaluating quality, accountability and meaning.
For companies, agents are an opportunity to build faster and leaner operating models. For startups, they can give a small team the leverage of a large organization. For individuals, they can be a personal assistant that helps you get started and see things through.
But agents do not remove the need for leadership, thinking or accountability. Quite the opposite.
The more AI does, the more important it is to know what it should be doing.
14. Practical frameworks and templates
Framework 1: A beginner’s AI agent experiment
Choose one scoped task. Good first targets: a draft weekly report, notes from a customer meeting, a competitor overview, draft email replies, a summary of an internal guideline.
Use this formula:
- Goal: What do I want to achieve?
- Sources: What information may the agent use?
- Boundaries: What must the agent not do?
- Output: In what format do I want the answer?
- Review: Which parts will I check myself?
Framework 2: Briefing an agent
A template to copy:
Your task is [task].
The goal is [goal].
The target audience is [audience].
Use only [sources] as sources.
Do not [prohibited actions].
Ask for permission before [risky action].
Return the result in [format].
Finally, list any uncertain points and anything a human needs to check.
Framework 3: Risk classification
Low risk — the agent may act independently. Examples: summarizing, drafting, brainstorming, formatting text.
Medium risk — the agent may prepare, a human approves. Examples: customer messages, quotes, analyses, internal reports.
High risk — the agent must not act without a clear human decision. Examples: payments, contracts, dismissals, hiring decisions, health interpretations, legal opinions.
Framework 4: Team rules for agents
A team should agree on at least these:
- What tasks may agents be used for?
- What data may be given to an agent?
- What must never be entered into an agent?
- When must an agent’s output be checked?
- Who is responsible for the final result?
- How are errors logged and learned from?
- What metric is used to assess the benefit?
Framework 5: A decision-making framework for leaders
Before deploying an agent, ask:
- Does this solve a real problem?
- Is the process already clear enough?
- Is the data in good shape?
- What happens if the agent makes a mistake?
- Who will notice the mistake?
- Who can stop the agent?
- How is success measured?
- What human skills does this strengthen?
- What skills could this weaken?
Framework 6: An agent chain for advanced users
Once basic use works, you can build a chain:
- The agent gathers background information.
- The agent summarizes the findings.
- The agent lays out the options.
- A human chooses the direction.
- The agent produces a draft.
- A human reviews and edits it.
- The agent finalizes the format.
- A human approves the publication or decision.
This is a good basic model because it combines the speed of an agent with human judgment.
The key takeaway
AI agents are not magic workers. They are a lever. A lever can lift a lot if it is placed in the right spot. In the wrong spot, it just bends the structure out of shape.
That is why the most important question is not: “What can an agent do?”
The most important question is: What should we let the agent do — and where must a human still carry the responsibility?