Artificial intelligence is no longer a technical issue. It is a strategic advantage that you can start building today, without knowing how to program and with real impact on your business.
Therefore, in this article we go through three levels of specific application of AIprompts, agentic workflows and agentsso that you can understand, decide and act judiciously:
One-shot prompting: complex tasks with a single instruction
A one-shot prompt is a clear prompt that gets the AI ??to solve a task on the first try. The key is in the precision, role and context given to the model.
Prompt: "You are a strategy consultant. You are inspired by Jeb Blount and Alex Hormozi. Write a short and professional email to win back a B2B client who has not responded in two months, remembering a proposal sent, without being pushy or aggressive."
Expected result: A clear, persuasive and appropriate email draft for a specific business situation. Something like:
Hello [Name], I hope you are well. A few weeks ago I shared a proposal with you about [project name] and I wanted to know if you had the opportunity to review it. If you want us to take it back or adjust anything, I'm available to chat. All the best,
Real applications of One-Shot Prompting in business
A well-formulated one-shot prompt can generate immediate value in multiple areas of the business. These are some of its most frequent applications:
• Strategic communication: writing commercial emails, speeches, LinkedIn posts, responses to clients, executive presentations.
• Content and sales: creation of personalized commercial proposals, landing pages, marketing materials and sales scripts adapted to each case.
• Analysis and synthesis: summary of meetings, extraction of key insights from documents, generation of points of view for internal analysis or presentation of results.
• Education and internal training: explanation of technical concepts in simple language for different audiences (non-technical teams, clients, partners).
• Design of ideas and scenarios: rapid generation of hypotheses, process structures, pricing models, product or campaign titles.
• Light operational support: task checklist, follow-up messages, standard responses to objections or frequently asked questions.
These applications allow AI to be integrated into the workflow without the need for complex systems, freeing up valuable time for higher impact tasks. Just access to ChatGPT, Claude or Gemini gets you started (even with the free version).
Business Impact of One-Shot Prompting
A well-designed one-shot prompt not only saves time on repetitive tasks, it can:
• Accelerate thought processes: Helps structure ideas, design initial strategies or visualize scenarios that would otherwise take hours of work.
• Reduce operational friction: Turn tasks that previously required multiple steps or intermediaries into a single agile movement.
• Unlock hidden value: Allows you to quickly explore new ways of communicating, selling, designing or solving problems, without the need for technical resources.
• Facilitate early validation: generate materials, hypotheses or simulations that can be tested with real stakeholders in less than an hour.
• Promote a culture of constant experimentation: lower the barrier to entry to trial and error, encouraging active learning and continuous improvement.
On a day-to-day basis, this translates into more speed, more clarity in deciding and more autonomy for non-technical teams, with a direct impact on productivity, strategy and operating model.
And this is just the beginning. When these prompts are chained into automated flows that react to the environment, we enter the realm of Agentic Workflowswhere AI not only executes tasks, but manages entire processes.
Agentic Workflows: flows designed by humans, executed by AI
Agentic workflows are chains of automated tasks that are executed sequentially and conditionally, but are not completely autonomous. They are designed by a human: choose the tools, define the steps and establish how and when each tool comes into play.
AI plays a tactical role within a broader system: it responds to prompts, transforms information, generates content, reviews and optimizes or executes tasks within a user-defined framework.
Example of Agentic Workflow applied to businesses
Context: An e-learning company launches online courses every month.
Workflow structured by the team:
Platforms like Zapier, Make, n8n make this possible. This entire flow is executed with models (OpenAI, Claude) and integrated tools.
AI executes tasks, but does not decide the flow, tools, or optimize the flow on its own without human instructions.
Strategic applications of Agentic Workflows in business
• Personalized commercial proposals at scale Automated generation and sending of proposals adapted to each client, integrating CRM data, personalized language and automated monitoring according to the recipient's behavior.
• Smart content production and distribution Creation of thematic content according to key dates, trends or audiences; automated publication on networks, blogs and newsletters; and active monitoring to adjust focus.
• Customer and employee onboarding with automated journeys Welcome flows, tool configuration, tutorials and adoption tracking, with personalized flows per profile.
• Real-time operational and management reports Automation of data collection, analysis and generation of weekly or monthly reports, automatically sent to the team or management with key insights and alerts.
• Intelligent management of administrative processes Detection and loading of accounting data, generation of recurring documents, monitoring of expirations and regulatory updates, all within scheduled flows.
• Advertising Campaign Management: It is possible to automate the generation of copies, creatives, loading and monitoring of campaigns across multiple channels, but real-time optimization still requires human supervision to make strategic decisions based on context and results.
Concrete business impact of Agentic Workflows
• Standardization of complex processes They allow unstructured tasks to be transformed into reproducible processes, with conditional logic and traceability, raising operational quality.
• Leverage without scaling the team They multiply the execution capacity while keeping the structure light, ideal for growing without increasing fixed costs.
• Structured and continuous experimentation Facilitates the simultaneous testing of hypotheses, message versions, channels or formats, accelerating learning with low risk.
• Reduction of operational dependency They reduce the need for human monitoring, reminders and manual coordination between tools or areas.
• Integration of heterogeneous tools They connect diverse systems (CRM, design, email marketing, BI) in a coherent flow, optimizing the use of the existing technological stack.
In summary: Agentic Workflows do not solve an isolated task. They execute complete processes, and that is why they are a strategic tool for real leverage. Intelligence is in how you think about them (the human being structures them) and the tools you define for each step.
AI agents: intelligent structures towards autonomy
An AI Agent is a digital entity that can receive a general objective, plan how to achieve it (flow), define the tools, execute multiple tasks, review, refine, adapt and obtain the results.
Unlike agentic workflows, it does not require a human to define step by step what to do. The agent reasons, decides and adjusts his behavior within certain limits.
The word Agents is the trend of 2025 and generates enormous confusion. It is used erroneously in all types of publications to refer to things as different as virtual assistants, GPTs and in most cases agentic workflows.
Like any trend, FOMO drags down many entrepreneurs. They want one, they are willing to pay, but they don't know what it is. Ask for an agent today and opportunistically they will sell you anything at a high price.
The important thing is to understand that today, the level of full autonomy of agents is just emerging in tools like AutoGPT, CrewAI or LangGraph, and still requires more advanced technical environments (development environments, APIs, tool configuration).
There are some demos, but few real agents are obtaining scalable results.
The trend, also among agents, is towards Low-code or No-code (lack of need to use code). Platforms like n8n and Make enable the option to venture with Agents.
Although these tools allow you to create AI agents without the need to program, it is essential to keep in mind that:
• The autonomy of these agents is limited to the workflow design.
• The ability to adapt and learn in real time is restricted.
• Integrating with language models requires a basic understanding of their operation and limitations.
What can be done? and what do I recommend?
Companies can now build basic multi-agent structures using humanly designed flows, which mimic agent behavior with very good results.
A fundamental concept is to start from the objective, understand the strategy, and design the simplest process to achieve the result.
What can be resolved efficiently with a one-shot prompt or a workflow does not need to reach the complexity and autonomy of the agents. In most cases, it is not even necessary.
Let's go back to the agents and review some possibilities:
A single agent with the ability to receive a general objective, choose tools, generate prompts, execute tasks, review results and try again if something fails.
Example: An agent is given the objective of analyzing customer feedback and proposing improvements to the product. Without human intervention, it accesses data, analyzes it with AI, writes a report with opportunities and sends it by email to those responsible.
Each agent has a specific function (e.g., research, writing, analysis, execution), and they communicate with each other to accomplish a common goal.
Example (CrewAI):
• Agent 1: Research market trends in online sources.
• Agent 2: Summarize findings and extract key insights.
• Agent 3: Write a commercial proposal with the data.
• Agent 4: Prepare a visual presentation ready to send.
They all coordinate automatically, without a human dictating the order or intermediate prompts.
An agent acts as a planner or strategist: it breaks down the overall objective into subtasks and delegates them to other agents who execute and report them. This structure allows you to scale in complexity without losing control.
Example (LangGraph or AutoGPT):
• The lead agent receives: Develop a monthly SEO content plan.
• Divide the work into: keyword research, editorial planning, writing, validation.
• Each sub-agent is in charge of a part, and the planner monitors and adjusts based on results.
Each agent operates with a level of autonomy over its domain, but is designed to share information and adjust its behavior according to changes in the environment or the rest of the agents.
Example (current experiments on LangChain or AutoGen):
• An agent monitors sales metrics.
• Another analyzes behavior on the web.
• Another adjusts advertising campaigns.
• A fourth modifies prices according to demand.
They all feed each other and update decisions without human intervention.
What does this mean for companies?
• A paradigm shift: from defined automation flows to distributed strategic autonomy in AI.
• The possibility of delegating objectives, not tasks, and trusting the process.
• An environment where tools collaborate with each other, reasoning, planning and adapting, as a human team would do.
Remember that:
• These structures are still in the experimental phase.
• Responsible and supervised use is key: it is not about releasing agents uncontrolled, but rather integrating them strategically into broader business systems.
• In general we do not need that much to achieve our objectives of applying AI for productivity, personalization, effectiveness.
• Scalability today has strong limitations (when we grow from hundreds, to thousands, to millions of users).
AI Agent applications in companies
• Personalized assistance in customer support An agent can receive a customer's query, review its history, identify the context (contracted product, previous interactions) and respond coherently, including personalized suggestions or escalating to a human being if the case requires it.
• Intelligent business opportunity management An agent system can scan new incoming leads, evaluate their potential based on historical data, assign them a priority, generate an initial proposal and activate a follow-up flow with personalized content.
• Autonomous optimization of internal processes Specialized agents can monitor key metrics (sales, deliveries, response times), identify bottlenecks and propose corrective actions based on best practices. They can even initiate minor changes automatically, such as reassigning tasks or modifying deadlines.
• Supporting management team decision making Agents configured with access to multiple sources can collect and summarize key information (industry news, internal performance, benchmarking), detect anomalies and formulate strategic recommendations. They can also prepare automatic reports for committee meetings.
Strategic Impact AI Agents
• Operational autonomy in cognitive tasks Agents allow repetitive and informed decisions to be delegated, reducing the team's burden in analysis, monitoring and coordination.
• Contextual adaptation (within limits) Although they lack common sense, agents can retry tasks, adjust their approach, and learn from simple mistakes, improving their effectiveness.
• Modular scalability New specialized agents can be added without redesigning the entire system. This makes it easier to grow with low marginal cost per unit of work.
• Continuous improvement cycle With each iteration, agents capture data on what worked and what didn't, generating feedback for their own learning or for human adjustments.
On the path of AI applied to business, AI Agents mark the next step. We are no longer talking about automating isolated tasks, but rather about delegating processes and decisions under supervision.
AI with meaning, strategy and autonomy
The key today is not to run out and "hire" agents with total ignorance (many times they are not necessary, they will offer us abusive prices and will probably sell us workflows, assistants or other alternatives).
Hiring today in most cases is like going to the grocery store without knowing the name of the fruits. But pay them 10 times more.
What is essential today? Incorporate AI into our organization's strategy.
To do so, at a time of fundamental change in companies, business people have the responsibility to internalize this new knowledge: experiment, put into practice, learn and not rush to "delegate to IT" or to a third party.
There are no longer excuses for not mastering chatGPT, prompting and knowing how to generate our own GPTs. No prior technical knowledge or code knowledge is required. Learning takes no more than 2 hours and practice.
And I highly recommend dabbling in Agentic Workflows using Make, Zapier or n8n. It requires no code and is much more intuitive than it seems.
Because?
Because what will make the big difference is the criteria, clarity and strategic vision that allows you to decide autonomously when, how and why to use AI in your business.
The competitive advantage lies in finding opportunities to generate value for actors with these technologies.
Before automating, creating agents, or investing in complex solutions, ask yourself the following questions:
• What part of my business model or operating model can be enhanced with AI?
• Where can I save time, gain productivity, fuel or improve decision making? Improve customer service or experience?
• What are the already validated processes that can be automated?
• Where can we generate value by personalizing the experience?
• What part of the interaction with clients could an assistant develop?
Instead of going out to hire, experiment. Add to your team. The tools are simple, economical, with graphical interfaces and natural language.
It is not about following trends, but about designing your own way of working, competing and creating value in a new context.
The true path is not technical: it is organizational, strategic and cultural.
Enjoy the process! It gives you infinite possibilities.
I hope you enjoyed the article. I read you in comments.
