AMESA is an enterprise software company that provides a platform for building, training, and deploying collaborative teams of autonomous AI agents designed to safely manage and optimize complex real-world physical systems. Pulse 2.0 interviewed AMESA founder and CEO Kence Anderson to learn more.
Kence Anderson’s Background
Could you tell me more about your background? Anderson said:
“I’ve spent the last decade focused on learning how to turn human expertise into systems that machines can actually use. Before founding AMESA, I led Autonomous AI Adoption at Microsoft, where I focused on digitizing expert industrial skills. Prior to that, and continuing to today, I’ve designed and deployed intelligent autonomous agents in real manufacturing and logistics environments, with more than 200 production deployments across companies such as Shell, PepsiCo, Delta, and several Fortune 500 manufacturers.”
“Along the way, I co-created a methodology called Machine Teaching, wrote Designing Autonomous AI, and spent significant time working with expert operators on the factory floor, shaping how I approach building AI systems for high-stakes environments.”
Formation Of The Company
How did the idea for the company come together? Anderson shared:
“The idea behind AMESA came from seeing the same failure pattern over and over again. Enterprises were investing heavily in AI, but pilots rarely made it into production. When they did, the systems often broke down because they’d never practiced real decision-making.”
“I realized the missing piece was teaching AI the way that humans learn. Experts don’t just know things; they practice, make mistakes, and refine judgment over time. AMESA was built to give AI agents that same path to competence, through simulation, feedback, and measurable performance before they ever touch live operations.”
Favorite Memory
What has been your favorite memory working for the company so far? Anderson reflected:
“One of my favorite moments came from our work with a global glass manufacturer on a production process so complex it involved more than 60 interdependent variables, all of which had to be tuned precisely to keep output within specification. Mastering that process typically took a human expert close to ten years.”
“Using AMESA, we enabled a team of AI agents to practice inside the Agent Cloud, learning through simulation while being continuously refined and taught by the expert who ran the process day-to-day. Over time, the agents began matching the expert’s decision-making across a wide range of operating conditions, ultimately augmenting the live process. This was critical for the company, as they, like the entire manufacturing industry, is experiencing a serious talent shortage.”
“When the plant manager told us, “We’ve never been able to automate this process until AMESA came along,” it was a defining moment for the company and a clear validation of our approach.”
Core Products
What are the company’s core products and features? Anderson explained:
“AMESA is a platform for training, orchestrating, and deploying teams of autonomous agents in enterprise environments.”
“At the core is our Agent Cloud, which gives agents a safe place to practice using simulations, digital twins, and real historical data. We pair that with our Agent Orchestration Studio, a visual, no-code environment where companies can design agent teams, assign skills, and supervise performance in real time. We also offer Assist Agents that help teams configure systems and interpret results, especially for organizations without deep AI expertise.”
“What ties it all together is our focus on measurable outcomes like yield, efficiency, and downtime, not just model performance.”
Challenges Faced
Have you faced any challenges in your sector recently, and how did you overcome them? Anderson acknowledged:
“One of the biggest challenges is expectation mismatch. There’s a lot of hype around ‘autonomous AI,’ but most tools on the market are still task automation or chat interfaces. That creates skepticism when enterprises don’t see real operational impact.”
“We’ve addressed this by being very disciplined about scope and proof. We don’t deploy agents until they’ve demonstrated competence in practice environments, and we tie performance directly to business metrics. That approach builds trust internally and with customers.”
Evolution Of The Company’s Technology
How has the company’s technology evolved since launching? Anderson noted:
“Early on, our work was focused almost entirely on industrial use cases, such as manufacturing, energy, and logistics. As the platform matured, it became clear that the same architecture could support a much broader set of enterprise decisions.”
“Today, AMESA is a horizontal platform for orchestrating teams of agents across operations, supply chain, and other high-stakes functions. The core philosophy hasn’t changed, but the scope and scalability have expanded significantly.”
Significant Milestones
What have been some of the company’s most significant milestones? Can you share any specific customer success stories? Anderson cited:
“AMESA has hit several major milestones that demonstrate both scale and tangible impact. Across our real-world deployments, our platform has delivered more than $100 million in combined realized and projected annual value for Fortune 500 enterprises.”
“Our agents have optimized some of the most complex and economically sensitive operations in industries like energy, manufacturing, and consumer goods. For example, in a single oil refinery, a team of coordinated agents increased daily blending profit by 16%, totaling over $21 million annually, with potential multipliers across additional sites. In a nitrogen manufacturing process, AMESA-trained agents outperformed the system’s industry-leading controller within 24 hours, generating projected annual gains of $1.2 million.”
Total Addressable Market (TAM)
What total addressable market (TAM) size is the company pursuing? Anderson assessed:
“We’re pursuing a TAM of over $480 billion in the global enterprise AI market. The AI Automation market alone will exceed $134B in just a few years, so we have an enormous opportunity to capture.”
Differentiation From The Competition
What differentiates the company from its competition? Anderson affirmed:
“Most platforms focus on building or connecting agents. We focus on teaching them on real data. AMESA gives agents a place to practice, fail safely, and prove competence before deployment. We also emphasize modular, transparent systems that mirror how human experts actually work, rather than black-box models that are hard to trust. Value-based outcomes are at the core of AMESA.”
Future Company Goals
What are some of the company’s future goals? Anderson emphasized:
“Our goal is to become the standard platform for operationalizing AI agents at scale. That means expanding into new enterprise domains, enhancing our simulation and training capabilities, and helping organizations capture and scale expertise across their operations.”
Additional Thoughts
Anything you want to add? Anderson concluded:
“Enterprise and industrial operations are entering a period of unprecedented complexity. Manufacturing processes are more interconnected, competition is more intense, and costs for energy and raw materials are increasingly volatile. At the same time, experienced talent is becoming scarcer just as the need for expert judgment is rising.”
“We believe this creates a generational opportunity for AI, not to replace human expertise, but to codify, preserve, and scale it. Decades of hard-won operational knowledge are at risk of being lost as workforces change. AI agents that can be taught, practiced, and validated offer a way to capture that expertise and make it available wherever it’s needed.”
“Done right, AI platforms like AMESA can certainly reduce bottlenecks and improve efficiency in the present. More importantly, AI will allow a new generation of workers to be upskilled faster than ever before, supported by systems that embody expert judgment. That’s the future we’re building toward at AMESA: AI as a force multiplier for human expertise in the most critical parts of the enterprise.”
