BraveLabs is a research lab and applied practice. We investigate multi-agent AI deliberation, cognitive bias in AI systems, and how AI architecture shapes organizational outcomes. We publish what we find and apply it in practice.
What the research shows about AI, organizations, and how they interact:
When multiple AI agents evaluate a candidate, assess a strategy, or review a proposal together, the structure of that deliberation matters. Which agents participate. How they update their opinions. How their individual assessments get aggregated into a recommendation. These design choices produce measurably different results from the same underlying evidence.
We study that phenomenon systematically. Our research examines how AI systems form opinions, where cognitive biases enter the process, and how deliberation architecture shapes the quality of collective decisions. We work in organizational settings where design choices carry real consequences: hiring, evaluation, strategic assessment.
Every question we investigate connects to something an organization has to decide.
Fourth Workshop on Knowledge-infused Learning, IJCAI 2024 · Barcelona, Spain
How does sentiment shape opinion dynamics in multi-agent AI systems — and what cognitive biases emerge when those systems evaluate people?
Sentimental Agents gives each AI agent a distinct Mental Model of Self. Sentiment analysis and a non-Bayesian update mechanism track how each agent's opinion forms and shifts across rounds of deliberation. The framework was applied to a simulated HR recruiting environment, where three agents in distinct organizational roles evaluated ten candidates and produced collective recommendations via three decision protocols: Borda Count, Tiered List, and Gut Feeling.
Agents showed a modest positivity bias — the positive sentiment slope ran approximately 29% steeper than the negative. Drift varied by agent role: the VP of Engineering and Plant Manager adapted more dynamically across the conversation than the CFO, suggesting role framing influences how a Mental Model of Self responds to deliberative pressure. Different aggregation protocols produced different candidate rankings from identical evidence.
Deliberation architecture shapes the recommendation. The number of agents, how they update, how their outputs are combined: these are design variables with measurable consequences. Organizations designing AI-assisted evaluation systems should treat these choices with the same care as model selection.
What this means for your firm: If you're using AI for evaluation, scoring, or selection — hiring, vendor review, strategic prioritization — the design of your system is producing different outcomes than you think. The architecture is the decision.
Three open research questions shaping current work. Not papers-in-progress — live problems we're investigating.
When a multi-agent system is embedded in a workflow, a governance question follows: who is making the decision? The answer changes depending on whether agents are advisory, evaluative, or executive, and where humans engage in the process. Most organizations deploying multi-agent AI today have not mapped their system against this typology. We are developing the framework.
In progressBefore you can optimize AI-assisted decisions, you need to know who's actually deciding. A Playbook makes that mapping explicit.
See the Playbook →The Artificial Board of Advisors concept asks this directly. An AI advisory panel drawing from behavioral economics, systems thinking, and organizational psychology offers a form of adversarial counsel that human boards rarely deliver consistently. We are investigating whether intellectual diversity in the panel architecture changes recommendation quality, and how that diversity can be designed.
In progressThis is the research behind our Playbook design. Better AI advisory structure produces better strategic decisions from the same leadership team.
See the Playbook →Role Diagnostics maps human-AI collaboration structures against the kinds of decisions organizations face. In some configurations, humans set the goal and AI executes. In others, AI surfaces options and humans decide. In others, both deliberate across multiple rounds. The research question is which arrangement produces better outcomes for different problem types.
In progressThis research directly informs how we design every Tool — matching the human-AI configuration to the actual problem your team is solving.
See the Tool →BraveLabs takes on a small number of applied R&D partnerships each year. Your organizational AI challenge becomes a research question we investigate together. The output is a working solution and a rigorous account of what we found.
You don't just get a consultant. You get access to the only firm that's actively studying what you're trying to solve.
Evaluation architectures — how AI systems score, rank, and select
Multi-agent deliberation — structured collective AI reasoning
Decision support systems — AI-assisted strategic and operational judgment
We take on a small number of applied R&D partnerships each year. In the first conversation, we'll tell you honestly whether your problem is a fit — and what working together would look like.