Úsvit autonómnych pomocníkov: Ako skrotiť digitálnu inteligenciu a získať späť čas, energiu aj náskokThe Dawn of Autonomous Assistants: How to Tame Digital Intelligence and Regain Time, Energy, and a Head Start

Just yesterday, AI was “reactive”: you type a question, you get an answer. Today, a new layer is emerging: the agent as a software “executor” that can understand a task, break it down into steps, select tools, and gradually complete it—often without you having to hold its hand every 30 seconds. This is exactly how IBM defines agents: an agent autonomously “understands, plans, and executes” and connects to tools, models, or systems as needed.

The Shift from Chatbots to Agents

This shift is not a marketing metaphor—it is an architectural change. A major survey on agentic AI describes the “Agentic AI Era (2022–present)” as a period where the capabilities of generative models are shifting from text to action: planning, using tools, memory, and, increasingly, the collaboration of multiple agents with distributed roles.

To be clear: an agent is not a “magical being.” It is a combination of (1) a model, (2) tools, (3) rules and supervision, (4) memory/context, and (5) the “agent loop”—that is, a cycle where the model proposes the next step, the system executes it, and returns the result. This is exactly how official documentation on computer use describes it: agentic “looping” is the core of autonomy.

Key implication: AI is ceasing to behave as an “answer searcher” and is beginning to act as a “work coordinator.” This trend is also evident in how major platforms are shifting AI toward proactive tasks: for example, an agent that can click, type, and scroll on the web in its own browser is already a reality in the form of “Operator” (research preview), where the user sets a goal and the agent executes it in the GUI.

And now comes the game-changing statement that transforms careers: when coding shifts from “writing” to “specifying intent,” a new kind of skill emerges. The term “vibe coding” has caught on as a name for a style where a person gives instructions in natural language and AI generates the code. This is how Google Cloud and IBM describe it as well: it’s a shift from syntax to intent, with the professional version requiring testing and taking responsibility for the result.

Interestingly, the coiner of the term “vibe coding,” Andrej Karpathy, is already publicly promoting another concept: “agentic engineering” as work where agents generate and modify code more autonomously, and the human role is more about management, quality control, and architecture than manual coding.

And note: just because something is called a “revolution” doesn’t mean it’s always faster. A randomized study by the METR research team, cited by Reuters, on experienced open-source developers showed that with “early-2025 AI tools,” developers were on average 19% slower—even though they subjectively felt they were faster. This is not an argument against AI; it is an argument for proper processes, proper expectation management, and proper oversight.

Building blocks of a new era

Imagine agents as a LEGO set. When you have the right pieces, you can build anything: from a personal assistant to an autonomous team that can prototype an application overnight. Today, these pieces are surprisingly standardized and accessible.

Three categories have driven the biggest shifts in practice: (a) agent SDKs and orchestration, (b) access to tools, including “computer use,” (c) local model execution and working with internal data via RAG.

Agent SDKs and orchestration have explicitly shifted from experiments to a “production mindset.” OpenAI reports that its original multi-agent framework, Swarm, has been replaced by the Agents SDK, which is production-oriented and recommended for real-world use.

Similarly, Microsoft has its own agent frameworks: AutoGen as a multi-agent conversational framework (with an academically published description and practical pilots) and the Microsoft Agent Framework as an open-source tool for creating, orchestrating, and deploying agents (Python and .NET).

If you want agents that run longer, have state and memory, and can be debugged like a serious system, LangGraph is often mentioned in the open-source world; it positions itself as an infrastructure for “long-running, stateful agents” and offers mechanisms for memory and observability.

Now for what’s most “wow” for the average person: an agent that can use a computer. Anthropic describes “computer use” as an effort to enable AI to use existing software “like a human” (clicking, typing, viewing the screen via screenshots), while openly admitting that it is currently slow and error-prone and that the model views the screen in a “flipbook-like” manner. This is an important detail: the future of agents is coming, but it’s not without its hiccups yet.

A similar direction is also evident in agents that are already performing real-world web tasks in a GUI (Operator) and in “ChatGPT agent” mode, where the agent can connect to connectors, conduct research, prepare a presentation, or process tasks—though the platform explicitly emphasizes that this introduces new risks, particularly prompt injection.

Thirdly: internal data. While an agent without context is a “clever student,” an agent with your data is a “senior colleague.” The technique that enables this at scale is often called RAG (retrieval-augmented generation): the model generates a response supported by documents retrieved from an external repository. The introductory RAG paper describes the core as a combination of parametric memory (the model) and non-parametric memory (the retrieval index) to make responses more specific and factual.

But there’s a security twist: OWASP explicitly states that neither RAG nor fine-tuning alone “fix” prompt injection—attacks via manipulated inputs remain a problem.

And here comes the “privacy” button: local models. Tools like llama.cpp show that inference can be done locally and models can be stored in efficient formats (e.g., GGUF) with support for various hardware.

Meanwhile, the ecosystem around running models locally has grown into a world of integrations: Ollama positions itself as a platform for connecting “open models” to applications and agents and lists thousands of integrations across coding, documents/RAG, and automation.

However, with on-premises deployment comes a harsh reality: security is no longer a “cloud problem”; it’s a configuration problem. Research by SentinelOne and Censys mapped approximately 175,000 publicly accessible Ollama hosts across 130 countries and notes that some of them have tool-calling capabilities—that is, the ability to perform actions, not just talk. The study also directly explains the mechanism: the default is localhost (127.0.0.1), but a single change of the bind type to 0.0.0.0 creates a public surface.

Practical scenarios you can implement this week

Here’s an important mindset: agents aren’t “toys”—they’re “processes in motion.” When used correctly, the result isn’t just 10 minutes saved, but mental capacity gained: less switching, fewer small decisions, fewer “open loops” in your head. That’s why it’s worth designing your first agent experiments to be safe, measurable, and gradually scalable.

A personal digital assistant with its own identity

The most practical “first agent” isn’t the one that has access to everything; it’s the one that has its own space and a clear role. Several platforms today are moving toward agents that can work with email/calendar via connectors, but at the same time give you back control at critical moments (such as during login or sensitive actions).

How to approach this so it’s usable right away:

• The assistant has a “work inbox” (not your main one) and plans in a “work calendar” (not your personal one).
• Every day, it prepares a brief “summary” for youthe three most important things, three risks, and three suggestions for simplification.
• Once a week, it will provide you with a retrospective: what took up time, what can be automated, and what requires your human judgment.
• This isn’t science fiction—it’s the organization of agency work with an emphasis on oversight and delegation.
• Autonomous cost optimization with a “leashed wallet”

The great added value of agents isn’t that they “know how to advise,” but that they can execute a series of steps: compare offers, check rules, propose a decision, and prepare an action.

To ensure security, take inspiration from how large agency systems handle “real-world consequences”: sensitive actions require explicit confirmation, and the platform has configurable restrictions (e.g., rejecting high-risk financial transactions).

In practice, this means: an agent may have a budget, but not the keys to the vault. A limited card, limited permissions, an audit log, and daily reports. This is precisely the spirit of the modern identity & authorization approach for agents, which is also recommended by the OpenID Foundation: separate authentication, delegation, and authorization, and handle tokens, revocation, and auditing for long-running tasks.

Local Data Guardian

If you work with sensitive documents (contracts, family finances, internal know-how), running the model locally is often the cleanest approach: the data never leaves your device. Practical overviews of running LLMs locally state it plainly: the reasons are privacy, control, and the ability to integrate with local folders or an API server.

At the same time, however, a lesson from the security world applies: “local” does not automatically mean “secure.” If a local server is accidentally exposed to the internet, it becomes a public computing resource that someone could exploit. Research by SentinelOne/Censys shows that this is happening on a massive scale, and it often involves hosts with tool-calling capabilities.

The Proactive Application Developer

The biggest shock for many people is that “coding” is no longer a binary “I know/I don’t know.” Vibe coding lowers the barrier to prototyping: you describe a need in natural language, AI suggests an implementation, you test, refine, and set boundaries.

But if you want this to be sustainable (and not just a weekend experiment), you need to transition from vibe to agentic: define quality standards, tests, security rules, and accountability. The METR study is an excellent “wake-up call” here: if AI is used without a clear process, it easily creates the illusion of speed, which in reality turns into more time spent on checking, fixing, and waiting.

Safe mode: rules that make AI an ally, not a risk

When an AI system shifts from being a “responder” to an “executor,” the scope of risks also grows. That’s not a reason to back down—it’s a reason to switch to professional guidelines.

First, the framework: The NIST AI Risk Management Framework describes AI as socio-technical, noting that both risks and benefits arise from the interaction of technical and social factors, and that risk management should be continuous. It also defines four core functions: GOVERN, MAP, MEASURE, MANAGE—not as a checklist, but as a framework for managing risk.

Then the “practical security glossary”: OWASP identifies specific threats of the agency era for LLM applications: prompt injection, leakage of sensitive information, “excessive agency,” leakage of system prompts, weaknesses in vectors/embeddings in RAG, disinformation, and unlimited resource consumption.

And now, a translation into “golden rules” that are 100% applicable.

The principle of isolation means: the agent does nothing on your main machine, account, or profile unless absolutely necessary. Even the official documentation for computer-use tools recommends a sandboxed environment (VM/container), because the agent interacts with the screen, clicks, and triggers actions.

The principle of separate digital identity means: the agent is a separate “machine identity” with its own permissions, not “you” in disguise. The OpenID report explicitly addresses agent and user authentication, permission delegation, token revocation, and auditing for asynchronous long-running tasks.

The principle of least privilege means: the agent is granted the minimum rights necessary to complete the task. NIST defines “least privilege” as limiting access rights to the minimum necessary to perform tasks. This rule is crucial precisely for agents, because an agent with tools can “act,” and an error turns into an action.

The “system prompt is not a firewall” principle means: never assume that instructions in the system are secret or impenetrable. OWASP warns that system prompt leakage is a risk and that sensitive information (keys, roles, permission logic) should not be included in prompts.

The principle of protection against prompt injection means: agent-based systems must account for the fact that malicious instructions may be “hidden” in the web, files, text, or metadata. OpenAI openly describes prompt injection as a key risk in agent-based modes and states that users should minimize exposure (e.g., by disabling connectors when not needed). OWASP also explicitly states that RAG and fine-tuning alone do not completely eliminate prompt injection.

The principle of regular supervision means: the agent is running, but you are in the “driver’s seat.” Even with agents that control the web, it is assumed that the user can take control (takeover) and that critical actions require confirmation.

And one more “hard-hitting” example worth remembering: publicly exposed local LLM hosts. Research by SentinelOne/Censys reveals a massive “unmanaged layer” of AI infrastructure without guardrails, where a significant portion of hosts also have tool-calling capabilities. This is precisely the situation where a good idea (local AI) becomes a security risk solely due to configuration.

And finally, regulation: in the European Union, AI is no longer just a technological issue, but also a legal one. The European Commission describes the AI Act as a risk-based framework: ranging from minimal risk through transparency to high-risk systems with strict requirements and “unacceptable risk” practices that are prohibited.

The Commission’s official website on the AI Act also provides a timeline: entry into force on August 1, 2024, full implementation on August 2, 2026, with exceptions (e.g., prohibited practices and AI literacy from February 2025, rules and obligations for GPAI from August 2025, an extended period for certain high-risk rules until August 2027).

For you, this means one simple thing: a secure agent isn’t a “nice-to-have”—it’s the foundation of professionalism. And a professionally configured agent is exactly what will give you a career edge, because most people will still be using agents “by feel” for a while—and you’ll be using them “for results.”

Vision: Where Autonomous Assistants Are Headed

The greatest potential of the agent era isn’t that AI will “replace people,” but that it will create a new attention economy: the human mind will shift from operations to decision-making, direction, and creation.

The short-term trend will likely look like this: more “agent mode” in everyday tools, more integration with calendars, inboxes, and knowledge bases, more work in longer execution cycles (asynchronous tasks), and more transparency through “tracing” and observability, because without these, agent-based systems cannot scale. This is evident in the fact that agent SDKs emphasize tracing/observability and that agent modes for users also promise “visibility into what the agent is doing.”

In the medium term, standardization of identities and access will be key: agents will have their own identities, delegated permissions, tokens, revocations, and auditing. The OpenID report directly highlights the specifics of agents: longer-running tasks, asynchronous operations, and the need for secure refresh without violating the principle of least privilege.

Technically, the “hybrid world” will also expand: part of the work will run in the cloud (scaling, connectors, performance), and part locally (privacy, sensitive data). Local LLM ecosystems are growing, but they will require professional security hygiene—precisely because the public exposure of local services is a real problem.

And from a human perspective? The most valuable skill of the future won’t be “knowing how to prompt.” It will be knowing how to:

• define the intent,
• set boundaries (security, budget, permissions),
• design a feedback system (quality measurement),
• implement control routines (reports, audits),
• and knowing when AI is slowing down and the process needs to be changed (the METR “illusion of speed” effect).
• Three questions that will dramatically simplify AI at work and at home
• What is my most time-consuming recurring workflow, and what in it is “human” vs. “mechanical”?

Detailed answer (what it should look like to help you): Take one process that eats up your time at least 2–3 times a week (email triage, reports, meeting prep, invoices, gathering materials, answering recurring questions). Break it down into steps and assign one of three categories to each step: “mechanical” (copying, summarizing, searching documents), “semi-mechanical” (drafting a response, structuring, grouping), “human” (final decision, accountability, ethics, signature). This is a practical implementation of the MAP/MEASURE/MANAGE framework: map the context, measure the risk, and set up management.

Why this will make your life much easier: The agent will not be tasked with “doing everything,” but rather with a specific mechanical module that is safe and measurable. This allows you to avoid “excessive agency” while also quickly evaluating ROI.

What identity and permissions will my agent have so that it is useful but not dangerous?

Detailed answer: Imagine that the agent is a new colleague to whom you’re granting access to systems. Would you give them your passwords? No. This is precisely where the golden rule lies: a separate agent identity, delegated permissions, short-lived tokens, revocation, and auditing. The OpenID report explicitly addresses the need to separate agent authentication from user authentication and capture the “intent to delegate” so you can demonstrate who granted the agent the right to act and why.

Why this will make your life much easier: Once you set it up, you can safely scale agents across multiple workflows without risking chaos and data leaks with every new idea. Moreover, this naturally brings you closer to what modern “AI literacy” and a risk-based approach in the EU require of organizations.

What is my “autopilot limit,” and what will the daily report of what the agent did look like?

Detailed answer: Define two lists in advance: “without confirmation” (can act on its own) and “only with confirmation” (must ask). This mirrors the approach of agency systems, which require explicit permission for actions with real-world consequences and prefer takeover mode for sensitive actions.

The daily report should have three parts: (1) what the agent did (steps), (2) based on what (sources/context), (3) what it recommends next (next step + risk). Without this, agent systems cannot be debugged and scaled in the long term—which is why agent SDKs emphasize tracing/observability and security frameworks highlight risks such as prompt injection or system rule leaks.

Why this will make your life much easier: Because you’ll gain a sense of control without micromanagement. The agent will save you time, but at the same time, you’ll never be “surprised” by what happened—and that’s the difference between professional AI use and the chaos that destroys trust.

What people often forget (and it’s good to address this today):

• Clarify which data is off-limits and never let the agent access it (neither via prompts nor system instructions).
• Set consumption limits (time, number of steps, costs) so the agent cannot cause a “denial of wallet” or get stuck in a loop.

Separate internal documents and RAG indexes by permissions, because vector databases are a separate security surface (multi-tenant leaks and embedding vulnerabilities are real risks).

Establish “AI literacy” as the standard (not just a tool, but a culture): who can delegate what, who monitors, what constitutes an incident, and what a rollback looks like.