Savalera Lab Overview
Mission, goals, and roadmap for the Savalera Lab research track.
Introduction
Savalera is an independent consultancy with a background in business transformation and systems delivery.
In 2025, we launched the Savalera Lab to integrate AI into our core business and deepen our focus on applied language model research. We study how language models and agents behave, perform, and evolve in real-world and simulated contexts, and use that knowledge to inform both research and implementation.
Why agents?
We see agents as a pragmatic path toward practical applications of AI. While core language model capabilities have advanced significantly, it is the structure and behavior of agents, how they make decisions, interact over time, and respond to feedback, that will shape how AI is used in daily work.
Agents are also the context where language models use tools, memory, roles, and collaborate. This creates new challenges: consistency, adaptation, coordination, and evaluation. Understanding these dynamics is essential if we want to apply AI safely and effectively across business, education, science, and the arts.
Lab goals
Our work focuses on both foundational understanding and practical outcomes. We research behavior and personality in language models, experiment with self-assessment and adaptation in agent workflows, and design architectures for structured multi-agent systems.
The lab's core activities include:
- Developing a structured research framework for studying multi-agent interaction, personality emergence, and behavioral traits in language model-based agents.
- Running experiments with conversational agents to observe how models change over time in response to prompts, roles, or interaction history.
- Building evaluation methods and datasets that measure behavior beyond task performance, covering personality traits, stress response, and internal reasoning.
- Developing reusable toolkits to support agent design, logging, and testing at scale.
- Applying our findings in product and client delivery work where custom AI workflows are designed and implemented.
We aim to contribute practical tools, share our results openly, and stay grounded in the day-to-day challenges of building AI systems that work reliably and make sense to real users.
Lab roadmap
Our 2025 launch roadmap is organized into four phases, each focusing on a core area of research and development.
Phase 1: Foundation and soft launch
- Timeline: Feb - May 2025
- Expected results: Create the research plan, set up the lab's technical toolkit, and run simulated dialogues to evaluate the behavior of language models and LLM-based agents.
Phase 2: Expanding agent adaptation and evaluation
- Timeline: May - July 2025
- Expected results: Explore single-agent behavioral dynamics through simulated dialogues, focusing on psychological safety, toxicity detection, bias emergence, and intervention strategies.
Phase 3: Agents in teamwork, decision-making and multi-agent dynamics
- Timeline: July - Oct 2025
- Expected results: Investigate multi-agent dynamics by simulating collaboration, competition, leadership, and group decision-making in conversational environments.
Phase 4: Scalable methods and evaluation framework
- Timeline: Oct - Dec 2025
- Expected results: Formalize evaluation metrics and frameworks to support scalable, repeatable experiments across diverse agent architectures and behaviors.
Research foundations
- Savalera Lab Manifesto outlines the motivation, values, and long-term vision behind the lab.
- Research Plan details the current research focus on language model and agent behavior, adaptation, and evaluation.