Multica: After Using It for a While, I Think I Get What They Are Doing With AI

Disclosure: I have no affiliation with Multica. This post contains no promotional links and is purely a record of my own experience with the product.

I have been using ChatGPT and Claude for a while, and one thing has always bugged me: a single chat with an AI is genuinely impressive, but the moment a task gets longer, has to be split into steps, or needs different “roles” to take turns, I end up doing the boring work myself — copying text between tabs, switching models, remembering where the last step left off.

Then I started using Multica. It is not another chat interface. It feels more like a platform where AI genuinely behaves like a teammate — picking up tasks, handing them off, reporting back. This post is a few notes from my own usage, not a product pitch.

Why I even bothered to try it

A few months back I was working on a long-form review of a tool. The workflow looked like this:

1. Ask AI #1 to outline the piece.
2. Switch to AI #2 to pull research and references.
3. Switch back to AI #1 to draft the body.
4. Switch to AI #3 to proofread and catch factual mistakes.

So I am bouncing between three or four tabs and manually ferrying text between them. The most painful moment was at step 4, when the proofreader flagged a factual error. I went back to the research from step 2 — and realized one of the citations was bogus, because I had not given the original sources in the first place.

Multica is built for exactly this kind of mess. Instead of talking to one AI, you are working with a small team. Each Agent has its own role and tools, and you can hand off tasks between them the way you would with real people.

The concepts, the way I actually use them

A few notes rather than a glossary:

• An Issue is a task. I create one, write what I want, assign it to an Agent, and it gets going. It feels like Jira, except the worker is AI.
• An Agent is the “person” doing the work. Each one has its own capabilities and tools — read code, search the web, run commands. I keep a few Agents around for different jobs: one for coding, one for research, one for review.
• A Squad is a small team of Agents that handle a bigger workflow. I have a “publishing” Squad: a writing Agent drafts, a review Agent proofreads, a publishing Agent pushes to the platform. A Leader coordinates who goes first.
• Mention is my favorite design. When I @ an Agent in a comment, it is not just a nudge — it actually triggers a run. It is the same muscle memory as @-ing a teammate in a chat, but the other side actually moves.

A real run, end to end

Take “write a blog post about Multica” as an example. Here is what actually happened:

1. I opened an Issue with the brief and assigned it to the writing Agent.
2. The writing Agent read the brief, checked the repo, skimmed my previous posts to match tone, and produced a draft.
3. I @-ed the review Agent in the comments. It checked logic and facts.
4. The review Agent left a list of issues in the comments; the writing Agent picked them up and produced another draft.
5. I read it, approved it, and flipped the Issue to done.

In the middle steps I did basically nothing. I did not switch tabs, I did not ferry text. The Agents handed work off to each other.

How it differs from AutoGen, CrewAI, and similar frameworks

I have been asked this a few times.

Frameworks are for when you want to write the orchestration as code — when you have hard customization requirements and engineers on hand to maintain it. Multica takes a different route: it productizes all of it. Issue boards, comments, permissions, scheduling — it is all out of the box. I do not write orchestration code at all.

For someone like me who wants to use AI without reinventing the wheel, Multica is a much more comfortable fit.

Some honest gripes after a few months

• A lot of tasks still take longer than I expect, because Agents sometimes have to wait on each other. If you are in a hurry, it can be slightly frustrating.
• For really custom workflows, you end up needing a framework approach. Multica handles the 80% of common cases well.
• Watch the billing model. Once you turn on Autopilot, the monthly bill can creep up faster than you think. I set a budget alert after the first surprise.

How I am using it now

My current pattern: the standardized stuff — weekly reports, meeting notes, draft outlines — goes to a Squad in Multica. It saves me real time. The creative, iterative work I still write a first pass myself, then let AI help me expand and revise.

The thing I keep coming back to is this: AI does not make me better at the work itself, but it lets me spend more time on the parts I am good at. That, more than anything else, is what I took away from using Multica.