The Two Barriers to Using AI Agents
If you want to get work done with an AI Agent, the first barrier is having one.
A Claude Code subscription costs $20 or $200 a month. A Codex subscription is another expense. Then you set up API keys and install the CLI. By the time everything is ready, you may have burned several hours before starting the actual work.
The second barrier is less obvious: one model isn’t enough.
Claude is strong at coding and long-context reasoning. GPT is good at general conversation and creative generation. Gemini is strong at multimodal understanding. DeepSeek is good value for batch work, and Kimi does well with long Chinese texts. Each model has its own strengths and its own price.
Using several models today means subscribing to and configuring each one separately, one subscription for Claude, another for OpenAI, another for Gemini, each with a different API key, invocation method, and billing. You switch between platforms and juggle several sets of credentials and configs by hand. It also wastes money: some tasks a cheaper model could handle, but if all you have is an expensive one, you run everything on it.
Tutti Agent takes on both barriers.
What Is Tutti Agent
Tutti Agent is an Agent built into Tutti · VM.
It already connects to model families from several providers, GLM, MiniMax, Kimi, DeepSeek, and more, with others being added over time. You don’t need a separate subscription or API setup for each one; Tutti Agent wires them up for you.
That means two things:
- You can start without your own Agent. New to AI Agents and unsure which to subscribe to? Try Tutti Agent first, it’s ready to use inside Tutti · VM.
- One entry point, many models on demand. No switching by hand between GLM, MiniMax, Kimi, and DeepSeek. Tutti Agent picks a suitable model for the task, or you specify which model runs which task.
Why Multiple Models Matter
Different models are good at different things
- GLM-5.2: strong general ability, good Chinese understanding, 1M-token context. Good for complex analysis, long-document comprehension, and general tasks.
- MiniMax M3: strong at conversation and creative generation. Good for copywriting, brainstorming, and open-ended exploration.
- Kimi K3 / K2.6: long-text processing is Kimi’s strength. Good for reading long documents and organizing large amounts of material.
- Kimi K2.7 Code: optimized for coding. Good for code generation, code review, and technical discussion.
- DeepSeek V4 Flash: fast and cheap. Good for batch processing, simple code generation, and data cleanup.
- DeepSeek V4 Pro: stronger reasoning. Good for complex tasks that need deeper thinking.
Use only one model and you end up applying its weak spots to another model’s strengths. GLM-5.2 for batch data cleanup costs more than it should; DeepSeek V4 Flash for a complex refactor may not be good enough.
Model prices vary a lot
This is a bill most people never add up.
The same task can cost several times more depending on the model. Batch-organizing user feedback might be a few cents on DeepSeek V4 Flash and several times that on GLM-5.2. Over a month, running everything on the most expensive model can cost three to five times more than a sensible mix.
The catch: most people can’t easily mix. You’d have to register on several platforms, top up separately, and configure multiple APIs. Just managing the accounts and keys is a pain, let alone switching between platforms.
Tutti Agent simplifies it: one Agent, many models behind it. You pick which model runs which task, or let Tutti Agent decide, with no extra subscriptions or API keys.
How You Actually Use It
Scenario 1: Assign models by task type
You’re on a product launch with all kinds of tasks.
For code architecture and refactoring, you use Claude Code (Fable) or Codex (SOR), which are strong on code. For marketing copy and user-research reports, you use MiniMax M3 for its creative and conversational strength. To summarize a several-hundred-thousand-character product doc, you use Kimi K3 or GLM-5.2 so the long context isn’t lost. To batch-organize user feedback, you hand it to DeepSeek V4 Flash, cheapest and good enough.
All of it moves forward in one Room. Each task runs on the model that fits it best, and the total cost is much lower than running everything on Claude Code (Fable).
Scenario 2: A multi-model pipeline
You’re doing a competitive analysis: read the competitors’ material, analyze the feature structure, write the comparison report.
You have Tutti Agent use Kimi K3 to read the long competitor docs and pull out features and design patterns. Then you pass the analysis to MiniMax M3 to organize it into a structured comparison report. Finally you have Kimi K2.7 Code review the technical-approach parts.
Several models, one pipeline, each stage on the model that’s best at it, with no manual carrying of content between platforms. The whole process and every output stay in the Room.
Scenario 3: Get started fast, zero config
You just heard about Tutti · VM and haven’t subscribed to any AI Agent. You create a Room, open Tutti Agent, and start.
No subscribing to Claude Code or Codex, no registering API keys, no configuring an environment. Tutti Agent is already wired to GLM, MiniMax, Kimi, DeepSeek, and more, you just say what you need.
Tutti Agent and BYOS
Someone will ask: if Tutti Agent already connects so many models, why still BYOS?
Because they solve different problems:
- Tutti Agent is for starting fast and mixing models flexibly. You use it with zero setup and switch models on demand, and its coverage keeps growing as more models (in and outside China) are added.
- BYOS is for the strongest coding model plus your personal capability assets, the configs, skills, and MCP servers you’ve built around your own Agent. BYOS lets you bring that accumulation in instead of losing it when you switch collaboration environments.
The ideal is both together: Tutti Agent for quick tasks and multi-model combinations, your own Agent (BYOS) for specialized work that needs your personalized setup, collaborating in one Room and referencing each other’s outputs with @.
