Imagine telling your project management app, “Plan out the next two sprints from this requirements document, and flag any tasks likely to run overdue,” and getting a detailed plan moments later. Or asking your coding environment, “Find any security flaws in this code and fix them,” and watching it draft a patch for your review. Or simply saying to your phone, “Schedule a vet appointment for my dog next week,” and having your AI assistant handle the calls and bookings. Star-trek-like scenarios like these are quickly moving from fantasy to reality. We’re entering a conversation-first era of software where AI “agents” don’t just chat with us – they act on our behalf.
What the agentic approach means in practical terms
The tech industry is abuzz with talk of “AI agents” and "agentic approach", but what exactly is an agentic approach to software? In simple terms, an AI agent is an automated system that can understand a user’s goal expressed in natural language and independently carry out tasks to achieve that goal. In other words, an agent goes beyond static responses: it has a degree of autonomy to interpret intent and act on it using software tools or by interacting with other systems.
This sounds straightforward, but in practice, “AI agent” means different things to different people. OpenAI, for example, has described agents as “automated systems that can independently accomplish tasks on behalf of users,” but also as “LLMs equipped with instructions and tools.” Microsoft draws a subtle distinction: it calls agents the “new apps” for an AI-powered world – tailored to specific expert domains – whereas its assistants are more general helpers for tasks like emailing. Anthropic’s researchers note the term can span everything from fully autonomous systems to prescriptive implementations that follow predefined workflows.
Simply put, adopting an agentic approach means designing software that asks the user what they want in natural language, then does its best to make it happen. Instead of a user painstakingly navigating menus, forms, and buttons, the AI agent figures out the intent behind the request and determines the necessary actions. It’s a bit like delegating a task to a smart digital intern: you describe the outcome you need, and the agent works out the steps to get there.
Crucially, an AI agent isn’t magic – under the hood it relies on large language models (LLMs) and sometimes other AI models, combined with integrations (APIs, scripts, databases) that let it execute operations. For instance, an agent might use an LLM to parse your command (“Add a bug to this sprint and assign it to Alice”) and then call project management APIs to create the ticket, or even simulate keyboard actions to click the right buttons if no API is available.
Defining agents across the tech landscape
It’s worth noting that the term “agent” has become a marketing buzzword lately – so much so that even industry insiders express frustration. The hype from CEOs is intense – with OpenAI’s Sam Altman predicting AI agents will "join the workforce" in 2025 and Microsoft’s Satya Nadella suggesting agents will replace certain kinds of knowledge work. Salesforce’s CEO even said his goal is to be the “number one provider of digital labor” via agentic services.
So, if the definition of “agent” feels murky, that’s because it is. For our purposes here, we’ll focus on the core idea: software that interacts through conversation and can take initiative to perform tasks for you.
From conversation to action: AI agents in natural-language interfaces
Conversation-first interfaces have already changed how we retrieve information – think of how we ask voice assistants for the weather, or query ChatGPT for a quick explanation. The next leap is agents that not only converse but also execute tasks in response to our requests. In a conversation-first UI, natural language becomes the command line for everything. AI agents serve as the behind-the-scenes operators that make those commands happen.
Customer service: agents resolving queries through intent-based actions
In the past, a user might click through a support website or navigate a phone menu. In a conversation-first approach, the user can just describe their issue in chat or voice. An AI agent analyzes the request, determines the intent, and then takes appropriate actions. Many companies are deploying exactly this kind of agent in early 2025. For example, Intercom’s Fin chatbot uses GPT-4 to understand support questions and resolve them instantly in up to 50% of cases. On the voice side, Yelp has begun rolling out an AI-powered voice agent to handle phone calls for restaurants and service businesses, including actions like adding a caller to a waitlist.
Developer tools: from Copilot to autonomous bug-fixers
Software development is another area being transformed. GitHub’s Copilot Chat allows developers to ask for help in plain English and receive code suggestions or fixes. Google DeepMind’s experimental dev agent Jules takes it further, integrating into GitHub workflows and handling tasks like issue triage, code generation, and test running, all under a developer’s supervision.
Productivity apps: personal assistants that draft, schedule, and summarize
Microsoft 365 Copilot and Google’s Gemini-powered assistants aim to offer a unified AI Copilot that can be called upon anywhere – to draft an email, summarize a report, crunch numbers in Excel, or schedule a meeting. These agents interface with the respective apps and remember user context to take more accurate and relevant actions.
The intent-driven command line of the future
Underneath these examples lies a common pattern: intent-based operation. The user expresses an intent and the agent translates that into operations on software. This could be database queries, API calls, or GUI manipulations.
Some agents even operate UIs directly. OpenAI’s browser agent Operator and Google’s Project Mariner can simulate clicks and keystrokes, accomplishing web tasks by navigating pages like a human. This means agents don’t always need a perfect API; they can imitate user behavior and still get the job done.
Benefits for users and organizations
1. For users: convenience, speed, and personalization
Conversational interfaces can make complex software more accessible. You no longer need to learn the quirks of each app – just express what you want. Agents handle routine tasks, provide personalized suggestions, and adapt to user preferences over time.
2. For organizations: scalability, efficiency, and insights
Software that simply asks “How can I help?” and acts on the answer can increase customer satisfaction while reducing support load. AI agents automate routine work, lower operational costs, and produce logs or insights that can guide strategic improvements.
Trade-offs and challenges in design and trust
Designing conversational UIs means accommodating a wide range of user inputs and guiding users without limiting their freedom. Suggestive prompts and hybrid interfaces (e.g. chat + buttons) are key to maintaining usability.
Users need to understand and verify agent behavior. Strategies include confirmation steps before major actions, visible reasoning steps, logs, and supervised autonomy where the user always reviews agent actions before they go live.
AI agents often handle sensitive data. Developers are introducing permission prompts, local data processing, and safeguards against issues like prompt injection. Enterprise deployments may isolate agent operations to preserve compliance and control.
Emerging patterns: personal agents and multi-agent ecosystems
Startups like Inflection (with its assistant Pi) and big tech players like Microsoft and Google are pushing toward persistent personal agents that learn from user habits and preferences. These agents are proactive, not just reactive.
In more complex systems, specialized agents work together: one plans, another executes, a third evaluates outcomes. This allows for modular, scalable automation. Some systems simulate debate or collaboration between agents to test different strategies before acting.
Amazon Bedrock Agents and similar orchestration layers help manage how agents call tools, pass tasks among themselves, and maintain shared context. These frameworks are the backbone for scalable, safe, multi-agent environments.
Looking ahead to an agentic conversation-first future
Conversational interfaces will likely become the default way users interact with complex systems. Just as GUIs replaced command lines for mass adoption, natural language could become the next leap in usability.
Expect new roles like AI trainers and conversation designers. Teams will need to develop skills in guiding and supervising AI effectively, balancing efficiency with ethical responsibility.
Since agentic systems can democratize access to software but may also amplify bias or enable automation at an uncomfortable scale, regulation, transparency, and user control will be critical to ensure a beneficial transition.
Designing software that talks and acts with Blocshop
AI agents are set to become an integral part of the conversation-first UI trend, turning chat interfaces into active, helpful participants in our goals. The agentic approach holds immense promise in making software more natural, powerful, and accessible, but it must be pursued thoughtfully, with attention to the human factors and ethical dimensions. We are witnessing the early stages of software that talks and acts – and if we guide it responsibly, it could lead to an era of computing that feels less like using a machine and more like collaborating with a capable colleague.
At Blocshop, we’re already seeing these shifts take shape across the software platforms we design and build. Our clients increasingly ask for user journeys driven by natural input, modular agent systems that can interface with complex APIs, and scalable foundations that let teams integrate AI-driven decisions with human control.
We combine backend architecture expertise with deep knowledge of UX to make conversation-first applications not only technically feasible, but reliable, fast, and human-centric. Whether it’s bringing LLM-powered assistants into enterprise platforms or building flexible orchestration for agent workflows, we help organizations move from theory to production.
If you’re thinking about bringing agentic interaction to your product or starting a new project that needs to align with the conversation-first future of software, let’s talk.
