Key Takeaways
- MAIA (Multi-AI Architecture) assigns two AI models — a Primary and a Secondary — to the same task within a single conversation.
- Three modes control how information flows between them: Parallel (both answer independently), Integrative (the Secondary extends the Primary), and Critique (the Secondary reviews the Primary).
- You always see two panels side by side. The Secondary never overwrites or blends into the Primary's answer — you compare both and decide what to use.
- The pairing principle is simple: choose the Primary best suited to the task, and a Secondary that compensates for its weaknesses.
- Running several models through AnyInsight.ai keeps everything aligned with company policy and leaves a full audit record — adding a model doesn't mean losing control.
- This is the final article in the five-part AI Essentials series.
1. Can you use two AIs at once?
The previous article described the character of each LLM. Some are strong at rigorous reasoning, some have immediate access to the latest information, and some understand local language and cultural context best. After reading the cheat sheet, one thought follows naturally:
If each of them has different strengths, can I use two at the same time and have them cover for each other?
This is one of the questions MAIA is designed to answer.
2. MAIA: two AI models working on one task
MAIA (Multi-AI Architecture) is the collaboration mode provided by AnyInsight.ai. It allows you to assign two AI models to the same task within a single conversation.
You designate two roles:
Figure 1: One task, two roles
- Primary. The side responsible for the main output. It can be an LLM, or it can be an AI agent you've created in advance. An agent already contains your data and your rules, which makes the division of labour fit your actual work more closely.
- Secondary. The side that covers the gaps. What it does depends on the mode you select: it can answer independently, build on the first answer, or specifically look for errors.
In one sentence: the Primary produces the main output, and the Secondary provides a different perspective and a different line of reasoning.
One point to note about roles: the Primary can be either an LLM or an AI agent. The Secondary must currently be an LLM. If you want the rules and knowledge inside one of your own agents to take part in the review, assign that agent as the Primary.
3. Which models does AnyInsight.ai support?
AnyInsight.ai currently supports the OpenAI ChatGPT family, the Google Gemini family, the Anthropic Claude family, Perplexity, xAI Grok, Mistral, and Taiwan's TAIDE, across different parameter tiers. A company can therefore apply the particular characteristics of different LLMs to different tasks and situations.
4. Three steps to start MAIA
Figure 2: Selecting a MAIA mode in AnyInsight.ai
- In the conversation panel, select the first LLM, or your own agent, as the Primary.
- Select "Single mode" to display the available MAIA modes, and choose the one you want.
- Select the second LLM as the Secondary.
The mode is fixed when the conversation begins. Once a mode has been selected for a conversation, it can't be changed during that conversation. To use a different mode, open a new conversation. Before you begin, therefore, take a few seconds to decide whether this task calls for a range of perspectives, for completeness, or for reliability.
5. How information flows in the three modes
AnyInsight.ai provides three MAIA modes by default. One important point is frequently misunderstood, so it's worth stating first:
The difference between the three modes is not how many panels are produced, which is always two. The difference is how information flows: whether the Secondary answers independently, or writes only after reading the response from the Primary.
Figure 3: How information flows in the three MAIA modes
In every mode, you see two panels side by side: one contains the response from the Primary, and the other contains the response from the Secondary. The Secondary never overwrites, absorbs, or blends into the answer from the Primary. Both are always presented separately, and you compare them, judge them, and decide what to take. This is one of the advantages of MAIA: the process is transparent. You always see what each of the two models said, rather than receiving a merged answer from a closed system.
Parallel mode runs the two separately. Integrative mode and Critique mode run them in sequence. The difference lies in the second panel: whether the Secondary reasoned independently, or read the Primary's answer first and then either completed it or examined it for errors.
Parallel mode
In one sentence: the two answer separately, and you compare them.
The task you submit, including the context you enter and the files you upload, is sent to the Primary and the Secondary at the same time, and each responds independently. You can then compare where the two responses agree and where they differ, and refine your question to clarify the differences. When the subject is contentious or ambiguous, this mode not only reveals the differing perspectives of the two models but may also expose a hallucination in one of them.
What you see: two panels side by side, containing the independent responses of the Primary and the Secondary, unrelated to each other, for you to compare.
Best practices:
- Brainstorming. Comparing different styles and ideas when naming a new product or writing a brand slogan.
- Analysis of contentious subjects. Examining model bias and avoiding blind spots on topics where values conflict, such as policy, law or ESG.
Integrative mode
In one sentence: the Primary writes first, and the Secondary continues and completes it.
After you submit the task to the Primary, including the context you enter and the files you upload, the Primary responds first. The Secondary then supplements or extends that response. This form of collaboration broadens the range of knowledge in the answer or deepens its detail. For tasks that are relatively well defined or require specialist knowledge, effective context engineering allows Integrative mode to deliver its full value.
What you see: two panels side by side, one containing the original response from the Primary and the other containing the expanded version produced by the Secondary after reading it. You compare the two and decide what to use.
Best practices:
- Writing a technical white paper. The Primary writes the structure and the argument, and the Secondary adds cases and best practices.
- Producing content across languages. The Primary produces the article, and the Secondary adds a summary and keywords to make it more complete.
Critique mode
In one sentence: the Primary writes first, and the Secondary reviews it.
After you submit the task to the Primary, including the context you enter and the files you upload, the Primary responds first. The Secondary then concentrates on identifying errors or points that could be improved. This form of collaboration shows you where the Primary's perspective is incomplete or flawed, gives you a view of the task from several angles, and may reveal a hallucination in the Primary. Selecting appropriate models for the two roles matters here: the Primary should have a higher level of reasoning ability, and the Secondary should be strong at verification.
What you see: two panels side by side, one containing the original response from the Primary and the other containing the criticism raised by the Secondary after reading it. You see both the answer and the objections to it, and you judge for yourself.
Best practices:
- Compliance review. The Primary examines a policy document, contract or privacy statement, and the Secondary identifies potential violations and risks.
- Security review. The Primary analyses code or data, and the Secondary corrects and improves it from the perspective of efficiency, readability or methodology.
Which mode should I use?
| Mode | What the Secondary does | What you see (two panels) | When to use it |
|---|---|---|---|
| Parallel | Answers independently, without seeing the Primary's response | The Primary's response and the Secondary's independent response | Brainstorming, contentious subjects, and any time you want to see differing perspectives |
| Integrative | Reads the Primary's response, then supplements and extends it | The Primary's draft and the Secondary's expanded version | White papers, in-depth content, and anything that needs both breadth and detail |
| Critique | Reads the Primary's response, then looks for errors and offers a different view | The Primary's draft and the Secondary's criticism | Compliance review, security review, and quality control before delivery |
A simple way to remember it: use Parallel for a range of perspectives, Integrative for completeness, and Critique for reliability. In every case you receive two panels rather than one merged answer.
6. The logic of pairing: use the Secondary to cover the Primary's weaknesses
Once the mode is chosen, you decide who plays each role. The principle is simple: select the Primary that's best at this task, and select a Secondary that compensates for its weaknesses.
Figure 4: A pairing example — cross-border contract review
| Task | Primary | Secondary | Mode | Why this pairing |
|---|---|---|---|---|
| Cross-border contract review | Claude (rigorous reasoning, quality in long documents) | Mistral (European regulation and data sovereignty perspective) | Critique | The Primary writes rigorously, and the Secondary checks it against local regulation and compliance |
| Real-time competitor research | Perplexity (current information with sources) | Claude (structured analysis) | Integrative | The Primary gathers the material, and the Secondary turns the material into analysis |
| Naming a new product | ChatGPT (general creativity) | Grok (social tone and current trends) | Parallel | Two different styles each produce a round of ideas, generating more options |
| Internal policy document review | Your own agent (company rules already built in) | Gemini (long context, reviews the whole document) | Critique | The Primary produces output according to company rules, and the Secondary looks for contradictions and omissions throughout |
If you're not sure how to pair them, there's a reliable starting point: use Critique mode, select the model you trust most as the Primary, and select a different vendor as the Secondary. Simply having a second model check the first intercepts a considerable number of errors and hallucinations.
7. Does adding a model make governance more complex?
No, and this is precisely the point of putting several models on one platform.
Whether you use a single model or MAIA, anything that runs on AnyInsight.ai is automatically aligned with the company's existing policies and compliance requirements, and leaves a complete supervisory and audit record. Conversely, if individual departments adopt different models without a common management platform, the advantages of a multi-model approach become inconsistent policies and fragmented data protection — a situation commonly described as Shadow AI.
The value of using several models rests on unified governance. The two belong together; they're not alternatives.
Adopting the platform as SaaS also removes the burden of integration and operation, substantially lowers the cost of initial investment and on-premises deployment, and allows applications to be put into production quickly.
Conclusion: don't ask just one AI
We used to ask which AI was the strongest. The honest answer is usually that it depends on the task.
MAIA is intended to remove the need to choose between them. Parallel mode gives you a range of perspectives, Integrative mode gives you depth and completeness, and Critique mode protects quality and manages risk. When two AI models divide the work and verify each other on the same stage, what you receive is not only a better answer — it's an answer you have more reason to trust.
The next time a task matters, don't ask only one AI. Have two of them work on it.
📚 This article is part of the AI Essentials series
Figure 5: The AI Essentials series learning path
The Series, Start to Finish
- Part 1 — So Many AI Tools. Where Should You Start?
-
Part 2 — Why Does AI Answer the Wrong Question?
- Part 3 — How Prompt Templates Boost Team Efficiency
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Part 4 — Which LLM Should You Actually Use?
- Part 5 — Solving Complex Tasks with Multi-AI Architecture (MAIA) ← You are here
