AI Agents for Business | Custom AI Agent Teams

What we're building

Two ways we put AI agents to work today: an open-source toolkit, and a vertical agent team built for one industry. The list grows as we go.

Open source

ZooGent

Build a team of agents once. Run it on the orchestrator you already use.

ZooGent is our open-source toolkit for creating AI agents and wiring them into a working team. It holds the patterns we have learned building agent teams for specific industries, so a team is designed once and runs on CrewAI, LangGraph, the OpenAI Agents SDK, or ZooGent's own runtime. No lock-in to one framework.

TypeScript Self-hosted Any LLM MIT license

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For content & SEO agencies

GEO Agent Team

Get brands cited in ChatGPT, Gemini, and Perplexity answers.

A team of seven agents built for B2B content and SEO agencies. It audits the client, tracks how AI engines answer the prompts that matter, mines real buyer questions, plans content clusters, drafts in the client's voice, and reports what moves. Generative Engine Optimization that runs 15 to 20 hours per client a week, handled by agents instead.

Research Baseline Monitor Question Scout Content Strategist Content Creator Publishing Performance Analyst

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In progress

More on the way

New agent products around specific jobs and industries.

We are building the next agent teams now: sales, support, document operations, and more. Have a workflow that a team of agents should own? Let us scope it with you.

Tell us about your workflow

How we build your agent team

We start from a business problem, not a tool. We map the workflow by hand, decide where an agent earns its place and where plain code is enough, then build the team and run it. Most projects reach a first working version in about two weeks.

Start from the problem. We scope one workflow that costs your team real hours, and build for that. Not AI for the sake of AI.

Start small, grow on demand. Begin with one agent or one small team. Add agents when the workload asks for it.

Right model for each job. Each agent runs the model that fits its task. A cheap model with clear instructions beats an expensive one without them.

What makes a team of agents work

A lone chatbot forgets, and it works alone. A team of agents shares four things, and that is what makes it dependable.

01

Tasks

One agent sends work to another with clear instructions. Like a direct message between coworkers.

Agent to Agent

02

Team Knowledge

A shared board where agents post what they learn. Once approved, the whole team can see it and use it.

Collective Intelligence

03

Memory

Each agent remembers what worked and what did not. A scoring system keeps useful memories close and lets the rest fade.

Personal Experience

04

Skills

Instructions written by people and shipped with the agent. Read-only. This is what makes a cheap model do the job of an expensive one.

Human Instructions

AI agents for your business operations

We set up AI agent teams for specific business problems. Works for small business and mid-size companies. Here are a few examples.

Customer Support Team

Reduce support load by 60-80%

AI agents for customer support that answer common questions from your docs, send hard cases to a person, and keep track of what got resolved.

Triage Agent Knowledge Agent Escalation Agent Follow-up Agent

Sales Operations Team

Qualify leads 10x faster

AI agents for sales that look up incoming leads, score them by your rules, pull in company info, and ping your sales team when someone looks ready to buy.

Research Agent Scoring Agent Enrichment Agent Notification Agent

Content Operations Team

Consistent content pipeline

Agents that find topics worth writing about, draft posts, check SEO, and publish on schedule across your channels.

Research Agent Writer Agent SEO Agent Publisher Agent

Data Processing Team

Automate document workflows

Agents that pull data from documents, check it for errors, update your systems, and build reports when you need them.

Extractor Agent Validator Agent Sync Agent Report Agent

FAQ

Devvela designs and builds teams of AI agents that handle real work in a business, from customer support and sales to content and back-office tasks. We start from a specific problem, map the workflow, and deliver a working agent team, usually with a first version in about two weeks.

An AI agent team, also called a multi-agent system, is a group of AI agents that work together instead of one chatbot doing everything. Each agent has a narrow role and runs the model that fits it. They pass tasks to each other, share knowledge, and remember what worked, closer to a small department than a single bot.

If your team repeats the same work every week, like answering the same questions, qualifying leads, or moving data between tools, an agent team pays for itself. You can start with one agent and one workflow, then add more as the workload grows. Most small businesses start with support, sales, or content.

ZooGent is our open-source toolkit for building AI agent teams. It holds the patterns we have learned building agent teams for specific industries, so a team can be designed once and run on the orchestrator a project already uses: CrewAI, LangGraph, the OpenAI Agents SDK, or ZooGent's own runtime.

Yes. Our GEO Agent Team is built for B2B content and SEO agencies. It researches a client, tracks how AI engines like ChatGPT, Gemini, and Perplexity answer key prompts, finds buyer questions, plans and drafts content clusters, and reports what is improving. That is Generative Engine Optimization work that otherwise runs 15 to 20 hours per client each week.

Ready to build your agent team?

Tell us what you want to automate. We will pick the right agents, build the team, and get it running. Most setups go live within two weeks.

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AI Agentsfor Your Business