AI agent ecosystems are expanding rapidly, and practical deployments are already reshaping software development and many business processes. Coding‑focused agents accelerate development cycles while task‑specific agents appear across business functions. The Portfolio AI & Digital Transformation conference on November 26 will feature the topic, with Kiss Gergely running a workshop on solving a concrete business problem using an AI agent.
What is an AI agent and how does it differ from chatbots?
An AI agent is more than a chatbot or a well‑crafted prompt: it is a software system that can autonomously plan and execute tasks toward a defined goal, make decisions, call external tools and APIs, and update its behavior based on feedback. Traditional chatbots primarily generate text and handle communication. In contrast, agents decompose complex tasks (for example, preparing a quarterly report), fetch data from enterprise systems, write or run code, and continuously refine their approach through corrective feedback loops. The degree of autonomy depends on the task and the permissions granted to the agent.
How an AI agent works in practice
Agents operate on a continuous feedback loop that can execute within minutes. The typical cycle:
- plan the next steps,
- invoke external tools, databases or other agents (e.g., ERP systems, code execution environments, APIs),
- execute actions,
- immediately evaluate outcomes and revise strategy as needed.
This loop continues until the goal is reached, a preset limit is hit, or the process encounters a decision point that requires human approval.
How to get started with agents in a company
Adoption is usually incremental. Many organizations deploy agents for single, well‑bounded tasks first, though collaborative agent teams are emerging. Practical advice: begin with a simple, verifiable task where outputs are easy to check. The article notes that some solutions — openAI Dots, Meta Muse, and Claude Duet — are not yet available in Europe, but accessible entry points exist, such as Hermes Agent or OpenClaw.
When might companies become more autonomous?
Agents can collaborate with other agents, forming teams that split work and involve external intelligent systems. Full corporate autonomy is a separate question: companies still need humans to set objectives, priorities and acceptable risk levels. That said, more internal workflows can become autonomous: agents can allocate work, execute tasks, cross‑check results and only escalate to humans at specific decision points or exceptional conditions.
Where human work remains essential
Strategy formation, goal setting, prioritization, and allocation of resources and responsibilities will continue to rely on human judgment and experience. Exception handling is particularly critical: when agents fail, get stuck, or encounter situations they cannot resolve safely, human intervention is necessary. If those human decisions and justifications are fed back into the agent processes, agents can learn to handle more exceptions autonomously over time.
Additionally, personal relationships, trust‑building with customers and partners, and other human interactions remain core human responsibilities that AI can support but not replace.
Responsibility, legal and ethical considerations
Wider use of AI agents raises legal and ethical responsibility questions. The concept of Organizational Singularity describes a potential transformation in which agents dramatically reduce execution and coordination costs, reshaping traditional corporate structures. In that context the term fiduciary wedge captures the gap between the agents’ increasing execution capabilities and the remaining human or organizational responsibility.
A practical example: an agent chain might fully process an administrative workflow — drafting a contract, preparing delivery confirmation, issuing an invoice and handling related administration — yet certain decisions and legal declarations still require human or legal entity approval and signatures. If damage occurs due to an agent error, a company cannot simply claim the agent erred and therefore no one is responsible. Responsibility must be allocable to people or organizations under law, contracts and corporate roles. As agent autonomy grows, it becomes increasingly important to define who is accountable for which decisions, where human approval is mandatory, and how to audit complex agent systems after the fact.
Conclusions
AI agents are spreading quickly and have the potential to change corporate operations substantially, particularly in software development and repetitive administrative tasks. Successful adoption requires iterative experimentation, starting with narrowly scoped tasks and establishing clear responsibility frameworks. While a growing share of workflows can become autonomous, strategic decisions, exception handling and legal responsibility will remain anchored to humans and organizations.
According to Kiss Gergely (Attrecto, president; Signifer Csoport, board member), the best way to understand agent behavior is through hands‑on use, so companies should run short cycles of experiments to identify where agents bring real business value. The upcoming conference and workshop provide forums to discuss practical deployments and responsibility arrangements in greater detail.
