Written by Gert Van Assche, CTO of Summa Linguae Technologies
Chameleon, one of many multimodal LLMs out there today, exemplified the astonishing multimodal capabilities of LLMs, integrating text, image, and audio processing. These advancements paved the way for smart QA systems capable of autonomously reviewing text and images in translated PDFs—a glimpse of what’s to come.
In parallel, frameworks like Salesforce’s CodeTree, launched in November 2024, showcased how agentic systems are revolutionizing tasks like code generation. CodeTree employs a multi-agent system comprising specialized agents—Thinker, Solver, Debugger, and Critic—to collaboratively navigate the code generation process.

Roles of The Agents:
- Thinker Agent develops diverse high-level strategies for tackling coding problems.
- Solver Agent translates these strategies into initial code implementations.
- Debugger Agent refines the code, using execution feedback and insights from the Critic Agent.
- Critic Agent evaluates code quality, guiding whether to refine, accept, or abandon solutions.
The paper includes a simplified version of instruction prompts used for Thinker, Solver, Debugger, and Critic agents.
By integrating these agentic elements, CodeTree achieves significant performance improvements across various benchmarks. The framework exemplifies how agentic systems can revolutionize complex tasks like code generation.

Agentic Features in CodeTree:
- Autonomous Decision-Making: Agents operate independently within their roles, contributing to the goal of generating correct and efficient code.
- Iterative Refinement: Debugger and Critic collaborate in a feedback loop to continuously enhance code quality.
- Dynamic Exploration: Thinker explores multiple strategies, while Critic dynamically guides exploration based on scoring and evaluation.
- Execution Feedback Integration: Combines execution-based and AI-generated feedback for comprehensive evaluation.
Preparing for the Agentic Future
The agentic approach represents a paradigm shift in how we address complex, multi-step tasks. However, it also underscores the need for careful evaluation and monitoring. From our experience building agentic pipelines for evaluating machine translation output, it’s clear that trust in technology’s judgment takes time to establish.
As we venture into 2025, the evolution of agentic LLMs will redefine the boundaries of AI capabilities. With robust safeguards and thoughtful oversight, these advancements hold the potential to transform industries and empower humanity in unprecedented ways.
Summa Linguae’s Data team supports LLM developers and integrators in training and evaluating their solutions. Our controlled production environment fosters seamless collaboration between subject matter experts and linguists, enabling our clients to innovate safely and confidently.
By integrating these agentic elements, CodeTree achieves significant performance improvements across various benchmarks. The framework exemplifies how agentic systems can revolutionize complex tasks like code generation.
📢 How do you see these advancements transforming industries in 2025?