Artificial intelligence

AI agents that act, not chat

Agents with tools, memory and human oversight for support, ops and back office. Whatever fails failure-evaluation doesn't deploy.

Tools MCP · function callingOversight Human handoffLanguages ES · EN · FR
In short

Slash Digital Lab builds AI agents for companies from Bogotá: agents that use real tools (not just answers), remember business context and know when to stop and call a person. We build them for support, ops, engineering and back office.

In production: Auto Fiche for Les Gentlemen du Jeu, which researches the web and generates product sheets with AI inside PrestaShop, and Mia, the AI copilot in the Réseau Mazette app. What works with agents and what doesn't: AI agents in production and prompt injection.

What we deliver

The agents we build

Every agent has tools, limits and an owner. None decides alone what it mustn't.

01

Support & care

Answers grounded in your knowledge base with citations, ticket handling and human handoff where needed.

02

Operations

Monitoring, alerts and automated actions on your systems, with an auditable log of every step.

03

Back office

Self-writing documentation, reconciliations, follow-ups and recurring reports.

04

Engineering copilot

Code review, test generation and technical documentation for your team.

How we work

How an agent reaches production

  1. Scope

    A process with volume and clear rules: where an agent genuinely wins. Written and measured.

  2. Tools & limits

    Which APIs and actions the agent may use, which it can't, and how it escalates to a human.

  3. Failure evaluation

    We break it on purpose: hostile prompts, dirty data, dead tools. Bad failures go back to design.

  4. Deploy

    Metrics, logs and a runbook; we review escalations weekly with your team.

Scope and deliverables

What the project delivers

Agent designArchitecture, tools, memory and the human-escalation policy.
IntegrationCRM, helpdesk, ERP or own APIs; identity and permissions by role.
GuardrailsModeration, topic limits and personal-data protection.
MetricsResolved, escalated, handling time and satisfaction, published weekly.
RunbookWhat to do when something fails, with owners.
LanguagesSpanish, English and French.
Frequently asked questions

The questions we hear often

What is an AI agent, and what isn't it?

Not a chatbot with a better prompt: it's software that decides steps, uses tools and delivers measurable outcomes. When unsure, it calls a person.

Do you use MCP and function calling?

Yes, plus custom integrations when the client's system lacks standard APIs.

How do you prevent false answers?

Citation-grounded responses, role-based action limits and failure evaluation before and after deploy.

What do you need to start?

A process with volume, rules and a system to integrate with; discovery then scopes it.

Who supervises the agent?

The client's team, with defined escalations and weekly metrics; we never run blind.

How much does an AI agent cost?

It depends on task volume, how many systems it must use, the level of human oversight and the model. We scope it in discovery, after a free first read, and it is best measured per resolved task: the real cost of AI.

Talk to Slash

Let's put it in production

Tell us your challenge. We reply within 24 business hours with an honest first read: if we can help, we'll say how; if not, we'll say who can.

I reply personally. No endless forms, no canned replies.

Message Esteban