Develop multi-agent solutions to solve problems and accelerate business outcomes
Enhance end-to-end digital products and experiences with solutions customized to your business challenges and reality.
Dec 1, 2025
Since the arrival of large language models in 2022, AI agents have moved from automation and simple task execution to becoming parts of coordinated and complex systems. Consequently, the emergence of multi-agent architectures, capable of executing workflows in an integrated manner, is allowing us to redefine how work is done in enterprises.
Despite the possibility, while companies publicize expressive results with the technology, numerous reports show that the challenge is not that simple: as indicated by an MIT study where 95% of projects do not reach the production phase. It is no coincidence that, amidst noise and promises, decision-making regarding Artificial Intelligence is marked by experimentation and risk.
Rather than underestimating it, we take this context seriously. We not only developed the AI Sprint method as a way to break the inertia, creating and validating a business-oriented solution in a few weeks, but we also adopt a stance of incremental deliveries in our solutions.
Inaction is as risky as unbridled experimentation. Therefore, it is fundamental to find structured ways to build solutions. Continuous technological advancement and the complexities associated with building an enterprise solution require development based on incremental, business-oriented deliveries.
Instead of launching as massive systems, our systems grow as they move important and specific business indicators. Even if promising, success depends on an implementation that adapts to the reality of the business, not the other way around.
The new paradigm demands technological updating, but it also demands consistent integrations with the company's visions and strategies—and not a reaction to hype or Fear Of Missing Out (FOMO).
How we develop multi-agent solutions:
Co-creating with business teams:
Immersed in the context and creating a qualified knowledge exchange with business teams, we validate a company-oriented proof of concept using our AI Sprint method.
Connecting the solution to specific and relevant results:
Using lean analytics methodologies, we develop with a focus on key indicators with the highest potential impact on the bottom line, incrementing the solution with each validated result.
Aligning strategy, design, research, and engineering:
We assemble a team with experience in applying technology in large companies, combining design, engineering, research, and strategy throughout the process, always co-creating with the company's business teams.
Applying Artificial Intelligence where it truly generates results:
By mixing deterministic models and Generative AI, we decrease exposure and the risk of hallucinations, potentiating AI capabilities where they truly impact the business.
Developing complementary agent flows and architectures:
Instead of a single large system oriented toward task automation, we work with specialized agents, optimized to take responsibility for specific tasks, which resolve problems in a coordinated manner.
Prioritizing integration and company autonomy with the solution:
In addition to empowering the company's people with the technology, we adapt our systems, frameworks, and models to the technological reality and needs of each company, preserving its autonomy.
Using advanced Prompt Engineering techniques:
Following our team's continuous curiosity, we work based on advanced Prompt Engineering techniques, constantly updated on new possibilities—as we explored in our article on APE.
Building with validated frameworks of different qualities:
Over the years, we have not only tested multiple frameworks but also validated proprietary structures that utilize advanced long chain graph techniques, adaptable to different business realities.
Making progressive, not reactive, improvements:
Using proprietary observability techniques and platforms, our systems grow according to the results achieved, accompanying continuous testing and alerts for exposure and risks.
Mapping the impact and ethical responsibility of the solution:
Our systems are born with an oriented ethical concern, where responsibility and eventual adaptation initiatives progressively accompany the development of solutions.
From business to technology
Our experiences demonstrate that to win fast, prioritization and close collaboration with business teams are necessary.
Enthusiasm for technology must not override business strategies, much less minimize the challenge of growing an enterprise. As we have learned — in iterations of our solutions — the true potential of the new paradigm is won in parts: from the specific to the comprehensive.
As our experience applying technology in companies like Mars, Movida, and Reckitt reveals, the best solutions are those that configure and adapt themselves to the company's processes, infrastructures, and strategies.