What exactly is an AI agent?
An agent doesn’t just respond. It takes action.
An agent can take a task, break it down into steps, carry out each step using different tools, check whether it has worked, correct it if it hasn’t, and deliver the final result to you. All without you having to manage every single decision.
A concrete example: instead of asking an AI to help you analyse last quarter’s sales, you tell an agent: ‘Analyse last quarter’s sales in the CRM, compare them with the competition and send an executive summary to the team via Slack.’ The agent accesses the CRM, performs the search, drafts the report and sends it. You don’t manage any of the intermediate steps.
The difference is fundamental. We have moved from conversing with AI to delegating entire tasks to it.
What’s currently available in the agent market
Claude Cowork, from Anthropic, is an agent designed to automate entire workflows, featuring a multi-agent system where one agent researches, another analyses and a third drafts in parallel. Its initial launch clients included Thomson Reuters, NYSE and PwC, which gives a very clear idea of who it is intended for.
Microsoft responded with Copilot Cowork, launched in its first pilot phase in spring 2026, which turns Copilot into an agent capable of executing complete workflows within M365. Its advantage remains the same: not just the model, but access to the full corporate context.
Genspark Super Agent demonstrates that agents also have a consumer dimension: planning trips, making bookings, creating complex multimedia content. In just 45 days since its launch, it reached $36 million in annual turnover.
OpenClaw, in the more technical segment, is an open-source agent that can operate continuously from a server, integrate with over 15 messaging applications and connect to different models depending on the task.
Why this is a game-changer
There is a phrase that sums up this moment very well: ‘2024 was the era of autocomplete, 2025 that of pair programming, 2026 is that of coordination.’
This means we are no longer in the phase of ‘AI suggests what to write’. We are in the phase of ‘AI executes complete processes and coordinates different tools to do so’.
The market reflected this very starkly when the announcement of Claude Cowork triggered falls of between 14% and 25% in the valuations of traditional software companies. Not because the technology is perfect today—the success rate for complex tasks is around 50%—but because the direction it is heading in is clear.
The sensible advice for anyone wanting to explore this: start with low-risk tasks. Summaries, file organisation, document drafts. Observe. Understand the limits. And scale up from there judiciously.
Governance matters more than the model
There is something I learnt before any technical benchmark: AI does not fix the mess you already have. It amplifies it.
An agent acting on your behalf inherits your permissions and your internal organisation. If your permissions are poorly configured, the agent may expose sensitive information. If your data is misclassified, it will work on erroneous data.
That is why any organisation wishing to implement AI agents seriously needs to do its homework first: data access audit, information classification, definition of permissions. AI comes later.
And a simple rule that applies to all environments: not all information should enter every system. The free versions of many tools may use part of the interaction to improve their future models. For contracts, customer data or internal strategy, always use enterprise environments or private deployments. It’s not paranoia: it’s basic data hygiene.
