Agentic AI for your business: from manual work to AI that does it itself
We design and build AI agents that perform tasks in your business autonomously. Inbox analysis, lead follow-up, product page optimization, invoice processing and more. From proof-of-concept within a week to production agent with managed operations on agreement.
In 2026 the big shift isn't that AI got smarter. It's that AI can actually do things now. An AI agent gets a goal, picks the right tools itself, executes actions and adjusts until the work is done. Not a chatbot waiting for questions, but a digital colleague that runs while you do other things.
At High Performing Company we build these agents for founders, SMBs and larger organizations. We work with human checkpoints, logging on every action, and a run-in period where a human watches before the agent runs autonomously. No black box, just predictable execution.
What is an AI Agent
An AI agent is software that combines three things: a language model as brain, tools with which it can perform real actions, and a goal it pursues autonomously. The difference with a chatbot is the word doing. A chatbot waits for a question and answers. An agent receives an assignment and works through until it's done, with checkpoints in the places you want them.
Concrete example: send a chatbot "what are my three most important emails today" and you get an empty answer because it has no access to your inbox. Give the same assignment to an agent with a Gmail connection and it opens your inbox, assesses messages on urgency, writes draft replies for the two most important and queues them in your drafts.
Who AI Agents are suitable for
AI Agents deliver the most value in organizations with recurring, predictable work that flows across systems. Concretely it works well for:
- Founders who lose too much time to admin, lead follow-up or inbox management
- SMBs with a marketing or sales team that repeats the same actions daily
- E-commerce companies with new products, customer questions, returns and reviews processed manually
- Larger organizations that want to automate a specific process without a long IT project
- Agencies wanting to speed up delivery using internal agents
AI Agents are less suitable for:
- Processes that change monthly (an agent only pays off if the task is stable enough to repeat)
- Work that requires final legal or medical accountability without room for handover
- One-off assignments (an AI workflow is enough there, not an autonomous agent)
Unsure whether your case fits? During the free intake of 30 minutes we assess it together.
What kind of agents we build
Our experience sits in a number of agent types that apply in virtually any business. Concrete examples:
The inbox analyst. Scans your email every morning, sorts into action/info/wait/noise, and writes draft replies for messages that need your attention. You start your workday with ten drafts instead of a hundred unread mails.
The lead enricher. Picks up new leads from your website form or CRM, scans LinkedIn and the company site, and delivers a briefing with conversation openers. Four minutes after sign-up your sales team calls prepared.
The e-commerce product launcher. Sets up new Shopify or WooCommerce products end to end: description in your brand tone, SEO title, meta description, alt tags, categories, social posts. What used to take a working day per week now happens in minutes per product.
The invoice processor. Reads incoming invoices, matches them to purchase orders, checks amounts and books them in your accounting package. When in doubt, into the human queue with an explanation why.
The competitor monitor. Visits the sites and channels of your main competitors weekly and delivers a report of what changed, plus a short interpretation of what it means.
The reporting builder. Pulls data from Google Analytics, Meta Ads, CRM and accounting, and prepares a one-page report weekly with numbers, observations and recommendations.
The customer signal agent. Monitors reviews, support emails and social mentions, classifies sentiment per topic, and pings you when a new signal appears that needs attention.
Custom agents. Much of what we build is bespoke. A publisher with daily content curation, an installation company with quote generation, a training provider with student intake. Have a specific process in mind, then we'll see during the intake if it fits.
How we work: the process
Building an agent isn't a weekend job, but also not a six-month IT project. We work in three phases.
Phase 1: Scope and proof-of-concept (1 to 3 days)
We start by defining exactly one task the agent will perform, which tools and data it uses, and how we measure success. At the end of phase 1 there's a working agent in a test environment, with sample data, ready for your substantive review. Here you test whether the agent does the right work at the right level.
Phase 2: Production and integration (2 to 5 days)
We connect the agent to your real systems (Gmail, CRM, e-commerce platform, accounting, or whatever applies), build human checkpoints where you want approval, and set up logging and monitoring so you can review every action. At the end of phase 2 the agent runs on your production environment under supervision.
Phase 3: Run-in period (2 to 4 weeks in parallel)
In this phase the agent runs with a human in the loop checking output. That's your team or our team, depending on agreement. Goal: build trust, discover edge cases, and fine-tune the agent until the error rate is acceptable. Only then does the agent go fully autonomous or in semi-autonomous mode.
Simple agents (one task, one system) we sometimes deliver in half a week. More complex multi-step agents can take 3 to 4 weeks. During the intake we estimate duration and investment concretely.
Which AI tools and infrastructure we use
The High Performing Company stack for agentic AI consists of:
- Claude (Anthropic) as primary language model for agents that need to reason complexly or execute code. Claude is in 2026 the strongest choice for agent work that demands accuracy and reliability.
- MCP (Model Context Protocol) as standard for connecting agents to tools and systems. MCP broke through in 2025 as the open standard with which AI talks to databases, mail, CRM and apps. This means agents aren't locked into one platform.
- Lovable for agents that need their own dashboard or interface (think of an agent that offers its own approval screen).
- Workflow orchestration with n8n, Zapier or custom code, depending on complexity. For simple agents we go no-code, for production-critical agents we build custom orchestration with logging and alerting.
- Hosting typically on Vercel or a dedicated container, depending on compute power and data sensitivity. For agents working with sensitive data we deploy EU hosting.
We're tool-agnostic and pick what fits per project. No reseller interest, no platform lock-in.
Investment
Investment depends on the type of agent and the scope. Simple single-task agents are a different story than a multi-step agent that works across multiple systems. During the free 30-minute intake we give an honest estimate based on what you want to automate, which systems are involved and what volume the agent must process.
Included in every build engagement: scoping, development, production deployment, logging and monitoring setup, guidance during the run-in period, and handover. Not included: any licenses for connected tools.
Operations after go-live
An agent in production isn't a static thing. APIs change, models get updates, and edge cases appear in week 6 that didn't surface during the run-in. For every agent we deliver there are two options: your team takes over operations (we deliver documentation and a handover training, plus 30 days of support included), or we make a tailored operations agreement. The shape is determined together, depending on volume, criticality and type of agent.
Why High Performing Company for your AI Agents
Three reasons:
One: We build agents ourselves, daily, in production. Not a consultant just drawing frameworks, but a team that knows which pitfalls show up in week three and how to design around them.
Two: Combination of marketing, e-commerce and strategy DNA. From the founders come years of experience with what agents must deliver commercially. That means we don't build for the tech, but for the business case.
Three: No lock-in. Code in your GitHub, MCP connections open, no platform fees. Want to continue alone or with another party in a year, that's fine without hassle.
Frequently asked questions
¿Qué es exactamente agentic AI?
¿Cuál es la diferencia entre un AI agent y un chatbot?
¿Cuánto se tarda en construir un AI agent?
¿Cuánto cuesta un AI agent?
¿Y si el agente comete un error?
¿Obtengo el código y la propiedad del agente?
¿Qué sistemas podéis conectar?
¿Cuál es la diferencia entre un AI agent y un workflow de Zapier o n8n?
¿También hacéis agentes para datos sensibles?
¿Construís agentes sobre sistemas existentes como HubSpot o Salesforce?
Ready to hand off work to AI?
Schedule a call or send a WhatsApp message. 30 minutes, we'll determine together which task in your business is best to automate first.
Want insight first into where AI offers opportunities in your operation? See the Marketing AI Scan. Want your own team to master AI tools? See the High Performing AI training. Need a website or business app instead of an agent? See AI web development in 4 days. AI Agents — la arquitectura técnica y los tipos de agente.