A roofing contractor in Charlotte heard the phrase "you can hire a team of AI agents" three times in one week. Once from a LinkedIn post. Once from a podcast. Once from his nephew who works in tech.
So he went looking. And he found four completely different products, at four completely different price points, all claiming to do the same thing.
One cost $22. One was free. One was $80 a month. One was included in a tool he already paid for. They couldn't all be right. They couldn't all be the same thing. And none of them explained, in plain terms, what they actually did differently.
This is the state of AI agent teams right now. The options are confusing, the marketing is thick, and most of the explanations assume you already know what you're looking at.
Here's the honest breakdown.
What "Agent Teams" Means in Plain Language
Before we compare products, let's get clear on what we're talking about.
A single AI agent is one instance of AI doing one job. You give it a task, it works on it, it gives you a result. Most people have used this — it's ChatGPT, it's Claude, it's any AI assistant handling a conversation.
An AI agent team is multiple AI instances working on the same project at the same time, each with a different role. One might research. Another might write. A third might review what the other two produced. They pass information between each other and coordinate — like a small team at a company, except every team member is AI.
We wrote about how one-person companies use these teams to run like a staff of ten. The short version: it works, and the sweet spot is three to five agents. This post answers a different question: which approach to agent teams makes sense for your situation?
The Four Approaches
Approach 1: Pre-Built Agent Packs ($22 each or $79/month)
A company called AgentPacks.ai sells seven pre-configured teams of AI agents. Each pack is a group of three agents with names, personalities, and defined roles.
The Content Creator pack has Mia (the strategist), Blake (the writer), and Jordan (the editor). The Dev Team pack has Marcus (the architect), Elena (the coder), and Sam (the tester). There's a Solopreneur pack, a Fitness pack, a Finance pack — 21 agents total across seven packs.
You buy a pack for $22 or subscribe for $79 a month to get all of them plus future releases.
What you're actually getting is configuration files. These packs run on Claude Code — specifically using its agent teams feature — and each agent is defined by a personality file that tells it how to behave, what to focus on, and how to interact with its teammates. You're not buying software. You're buying pre-written instructions for AI agents that already exist.
The pitch is accessibility. Someone who doesn't want to figure out how to configure agent teams from scratch can buy a pack, drop it into their setup, and have a working team in minutes.
The catch: you still need Claude Code installed and a subscription to use it. The packs are configuration files, not standalone products. And the named personalities — Mia, Blake, Jordan — are cosmetic. They make the experience feel more tangible, but underneath, every agent is the same AI model following different instructions.
Best for: Someone already using Claude Code who wants agent teams without writing their own configuration files. The lowest barrier to entry.
Not for: Someone who doesn't already have a Claude Code setup. The packs are add-ons, not complete products.
Approach 2: Open Source Pipeline ($0)
Ryan Carson — the founder of Treehouse, an online coding school that taught millions of people to code — built an open-source tool called Antfarm. It's free, it's on GitHub, and it takes a fundamentally different approach to agent teams.
Where the agent packs give you personalities, Antfarm gives you pipelines.
A pipeline is a fixed sequence of steps that agents run through in order, the same way every time. Antfarm ships with three built-in workflows: feature development (seven agents), security auditing (seven agents), and bug fixing (six agents).
Here's the key difference. In the pack model, you spin up a team and let them figure out how to collaborate. In the pipeline model, the collaboration is pre-defined. Agent 1 always does step 1. Agent 2 always does step 2. The output of each step feeds into the next. There's no improvisation.
Carson's argument for this is compelling. He says — and he's right — that when you let AI agents freelance on how to collaborate, you get inconsistent results. Sometimes brilliant, sometimes a mess. A pipeline trades flexibility for reliability. The feature-development workflow does the same seven steps every time: understand requirements, design the approach, write the code, review the code, write tests, verify the tests pass, and do a final check. Same steps. Every time.
It also has a verification loop built in. Each agent checks the previous agent's work before proceeding. If agent 3 finds a problem with what agent 2 produced, the pipeline loops back. This is a meaningful safety net — it catches errors that a personality-driven team might miss because none of the "characters" are specifically assigned to be the skeptic.
Best for: Developers or technical teams who want repeatable, auditable AI workflows. If you need to prove that the same process ran the same way every time — for compliance, quality assurance, or just peace of mind — this is the right model.
Not for: Non-technical users. Antfarm is a command-line tool that requires a terminal, some comfort with YAML configuration files, and a working Claude Code installation. If you don't know what those words mean, this isn't for you yet.
Approach 3: Visual Canvas ($16-$80/month)
Spine AI takes yet another approach. Instead of configuration files or command-line pipelines, it gives you a visual workspace — a canvas where you drag and drop blocks to build AI workflows.
Each block represents a capability. A Chat block talks to AI. A Research block searches the web. An Image block generates visuals. A Slides block creates presentations. A Memo block writes documents. You connect the blocks together, define the flow, and let it run.
The pitch is that non-technical users can orchestrate AI agents without writing code or configuration files. You see the workflow visually, you can rearrange it by dragging blocks, and you can watch the agents work through each step.
Spine also offers access to over 300 AI models through a single subscription — not just Claude, but GPT-4, Gemini, open-source models, image generators, and more. The $16 starter plan and $80 professional plan include model access, so you're not paying separate subscriptions to each AI provider.
Now for the caveats. And there are several.
First, your data goes through Spine's servers. Unlike the packs and the pipeline — which both run locally on your computer — Spine is cloud-hosted. Every document, every prompt, every piece of content you feed through the system passes through their infrastructure. For a blog post or a marketing brainstorm, that's probably fine. For sensitive business data, financial records, or anything confidential, that's a real concern.
Second, Spine doesn't have SOC 2 or HIPAA compliance. If you're in healthcare, finance, legal, or any industry where data handling is regulated, Spine is not an option right now. Full stop.
Third, Spine is built for research, content, and analysis — not for writing code or building software. It's a thinking tool, not a building tool. If you want agent teams that help you develop a product, this isn't it.
Best for: Non-technical users who want AI agent workflows for content creation, research, and business analysis. The visual interface is genuinely more accessible than anything else on this list.
Not for: Anyone handling sensitive data, anyone in a regulated industry, or anyone who needs agents that can write code and interact with their computer.
Approach 4: Built-In Agent Teams ($0 extra)
Claude Code — the same platform that the packs and pipeline run on — has its own built-in agent teams feature. If you already have a Claude Code subscription, you already have access to it.
The built-in system works differently from all three options above. Instead of pre-defined personalities or fixed pipelines, it uses what's called a peer network model. You describe what you want done. A lead agent breaks the work into tasks. Teammate agents claim tasks from a shared list, work on them independently, and coordinate when their work overlaps.
There's no script. No fixed pipeline. No named characters. The lead agent figures out the decomposition on the fly, and the teammates self-organize around the work. If one teammate finishes early, it picks up the next available task. If two teammates' work overlaps, they coordinate directly.
This is more powerful than any of the packaged options — and more unpredictable. The adaptive approach means the system handles novel problems better, because it isn't locked into a pre-defined workflow. But it also means results vary more between runs. The same prompt might produce different task decompositions, different coordination patterns, and different final outputs.
Best for: Technical users comfortable with Claude Code who want maximum flexibility. This is the most capable option on the list.
Not for: Anyone who wants consistency, simplicity, or a visual interface. The built-in system is powerful but has the steepest learning curve.
Side-by-Side Comparison
| Pre-Built Packs | Open Source Pipeline | Visual Canvas | Built-In Teams | |
|---|---|---|---|---|
| Cost | $22/pack or $79/month | Free | $16-$80/month | Included with subscription |
| Runs where | Your computer | Your computer | Their cloud | Your computer |
| Technical skill needed | Low-medium | High | Low | High |
| Data stays local | Yes | Yes | No | Yes |
| Best for | Quick start, content, small business | Repeatable dev workflows | Research, content, non-technical users | Complex, adaptive work |
| Consistency | Medium | High | Medium | Lower |
| Compliance-ready | Yes (local) | Yes (local) | No (no SOC2/HIPAA) | Yes (local) |
The Honest Truth: Most Businesses Don't Need Agent Teams Yet
Here's where we're supposed to tell you which one to buy. We're not going to do that — because most businesses reading this don't need any of them right now.
Agent teams are a power tool. Like a table saw or a CNC machine, they're incredibly capable in the right hands on the right project. But most woodworking doesn't require a CNC machine. Most business AI work doesn't require agent teams.
A single AI agent — one instance, doing one job — handles 80% of what small businesses actually need from AI. Answering customer questions. Drafting emails. Summarizing documents. Scheduling. Data entry. We wrote about how most businesses need Level 2, not Level 5, and that's still true. Agent teams are Level 4.
If you have a single agent that's working well for you, and you're wondering about teams, the answer is probably "not yet."
When You Actually Need a Team
There are specific scenarios where a single agent isn't enough. Here's when agent teams earn their cost.
Parallel research. You need to evaluate three competing vendors, research three market segments, or analyze three strategic options. A single agent does these sequentially — one at a time — and the third analysis is always colored by the first two. A team of three agents researches them simultaneously and independently. No anchoring bias. Better comparisons.
Content production at volume. You need to produce a blog post, three social media posts, an email newsletter, and a video script — all from the same source material but each tailored to its platform. A team of agents, each specialized for a platform, produces more platform-native content than a single agent switching between formats.
Adversarial review. You want one agent to build something and another agent to tear it apart. A sales page drafted by one agent and critiqued by another produces better output than a single agent trying to be both creative and critical. The friction between them is where the quality lives.
Multi-component projects. You're building something with clear, separable parts — a database, an API, a user interface. Each agent owns one component and coordinates at the boundaries. This is how one-person companies ship software that used to require a team.
If none of those describe your situation, stick with a single agent. You're not missing out.
The Security Question Nobody Asks
Three of the four approaches on this list run on your computer. Your data never leaves your machine. That matters — a lot.
Spine AI is the exception. Everything goes through their servers. And they don't currently have SOC 2 certification, HIPAA compliance, or published data retention policies. That doesn't mean they're careless — it means they're early. Compliance infrastructure is expensive and time-consuming to build, and startups often don't have it in year one.
But for any business handling customer data, financial information, medical records, or anything that has regulatory requirements, "they're probably fine" isn't good enough. Until Spine publishes a clear security posture, it should be limited to non-sensitive content work.
The local options — packs, pipelines, and built-in teams — all inherit the security model of whatever AI subscription you're using. Your prompts go to the AI provider but not to a third-party intermediary. That's a meaningful difference. We evaluate every AI tool through a security lens — don't skip this step just because a product looks slick.
Start Here
If you've read this far and you're still not sure which approach is for you, start with the simplest possible version.
Take one task you do repeatedly. Something that takes 30-60 minutes. Not your most complex workflow — just a routine task that feels like it should be faster.
Set up one AI agent to do that task. Not a team. One agent, one job.
Get that working reliably. Trust the output. Understand the cost. See where it falls short.
Then — and only then — ask whether a second agent reviewing the first agent's work would make it better. That's the natural entry point to teams: not from a product page promising 21 named AI personalities, but from a real limitation you've hit with a single agent on real work.
The roofing contractor in Charlotte didn't need a team of AI agents. He needed one agent that could handle his customer intake calls. Once that was working — once he saw the pattern — he added a second agent to follow up on estimates that went cold. Two agents, two jobs, both doing work that used to fall through the cracks.
Not a pack. Not a pipeline. Not a canvas. Just two agents that earned their place by solving problems he actually had.
Start there.
Blue Octopus Technology helps small businesses figure out which AI tools are worth their money — and which ones are just clever marketing. If you're evaluating agent products and want a straight answer, let's talk.
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