Powerful AI tools are becoming aggregators like Manus, which intelligently select the best underlying model for a specific task—research, data visualization, or coding. This multi-model approach enables a seamless workflow within a single thread, outperforming systems reliant on one general-purpose model.
Related Insights
Recognizing there is no single "best" LLM, AlphaSense built a system to test and deploy various models for different tasks. This allows them to optimize for performance and even stylistic preferences, using different models for their buy-side finance clients versus their corporate users.
Rather than relying on a single LLM, LexisNexis employs a "planning agent" that decomposes a complex legal query into sub-tasks. It then assigns each task (e.g., deep research, document drafting) to the specific LLM best suited for it, demonstrating a sophisticated, model-agnostic approach for enterprise AI.
The path to robust AI applications isn't a single, all-powerful model. It's a system of specialized "sub-agents," each handling a narrow task like context retrieval or debugging. This architecture allows for using smaller, faster, fine-tuned models for each task, improving overall system performance and efficiency.
Rather than committing to a single LLM provider like OpenAI or Gemini, Hux uses multiple commercial models. They've found that different models excel at different tasks within their app. This multi-model strategy allows them to optimize for quality and latency on a per-workflow basis, avoiding a one-size-fits-all compromise.
Exceptional AI content comes not from mastering one tool, but from orchestrating a workflow of specialized models for research, image generation, voice synthesis, and video creation. AI agent platforms automate this complex process, yielding results far beyond what a single tool can achieve.
The comparison reveals that different AI models excel at specific tasks. Opus 4.5 is a strong front-end designer, while Codex 5.1 might be better for back-end logic. The optimal workflow involves "model switching"—assigning the right AI to the right part of the development process.
Contrary to the trend toward multi-agent systems, Tasklet finds that one powerful agent with access to all context and tools is superior for a single user's goals. Splitting tasks among specialized agents is less effective than giving one generalist agent all information, as foundation models are already experts at everything.
The belief that a single, god-level foundation model would dominate has proven false. Horowitz points to successful AI applications like Cursor, which uses 13 different models. This shows that value lies in the complex orchestration and design at the application layer, not just in having the largest single model.
Like Kayak for flights, being a model aggregator provides superior value to users who want access to the best tool for a specific job. Big tech companies are restricted to their own models, creating an opportunity for startups to win by offering a 'single pane of glass' across all available models.
Replit's leap in AI agent autonomy isn't from a single superior model, but from orchestrating multiple specialized agents using models from various providers. This multi-agent approach creates a different, faster scaling paradigm for task completion compared to single-model evaluations, suggesting a new direction for agent research.
