Agent Collaboration Trumps Model Scale for AI Productivity

The future of AI isn't about building bigger models; it's about fostering collaboration. This conversation with Ece Kamar, a veteran researcher at Microsoft, reveals that the true power of AI lies not in monolithic intelligence, but in the emergent capabilities of interconnected "societies of agents." The non-obvious implication? Focusing solely on model size is a strategic blind spot. Instead, organizations that prioritize building systems where smaller, specialized agents communicate and collaborate will unlock unprecedented productivity and competitive advantage. Business leaders who understand this shift can gain a significant edge by redesigning workflows and investing in the foundational "agentic stack" now, rather than chasing the diminishing returns of simply scaling up individual models. This insight is crucial for anyone looking to navigate the next wave of AI adoption and build truly impactful AI-driven operations.

The Network Effect: Why Collaboration Trumps Scale in AI

The prevailing narrative in AI for the past couple of years has been a relentless pursuit of scale: bigger models, more parameters, and the promise of Artificial General Intelligence. However, this conversation with Ece Kamar, a researcher with two decades of experience in AI agents, pivots sharply, arguing that the real breakthrough isn't in individual model size, but in the collective intelligence of networked agents. This "society of agents" concept suggests a fundamental shift in how we should approach AI development and deployment, moving from a focus on solitary, powerful entities to distributed, collaborative systems. The immediate implication for businesses is clear: investing in the infrastructure and mindset for agent collaboration will yield far greater returns than simply chasing larger models.

Kamar traces her journey from a PhD student dreaming of AI agents to her current role at Microsoft Research, highlighting how advancements in Large Language Models (LLMs) have finally made those decades-old dreams tangible. "Many of the problems we are working on right now are actually decades old," she notes, emphasizing that the core challenges of agentic AI have long been understood. The advent of models like GPT-4, however, has provided the "general problem-solving power" that was previously missing, transforming prototypes into deployable realities. This shift from theoretical possibility to practical application is the bedrock of the current agent revolution.

The core idea of a "society of agents" moves beyond the current paradigm of a single user interacting with a single AI assistant. Kamar envisions a future where agents act as proxies for individuals and even entire departments, negotiating, collaborating, and executing tasks in concert. This isn't just about scheduling meetings; it's about complex workflows involving legal, sales, and operational agents working alongside their human counterparts. This vision challenges the conventional wisdom that more business value comes solely from larger, more capable individual models. Instead, it posits that the true value lies in the emergent properties of these interconnected systems.

"When we look into the agents today, we don't imagine them, they are going to be working in isolation. That would be actually really silly. But what we imagine these agents to do is actually gain the abilities to be able to talk and work with other agents and people to really guide what the future of productivity, future of commercial activity, and future of interactions are going to be like."

-- Ece Kamar

The technology enabling this transition is multifaceted. Kamar points to the evolution from LLMs primarily focused on language generation to "reasoning models" that are trained on actions and learn from feedback in simulated environments. This includes the development of smaller, more specialized models, like Microsoft's Forest 7B, which can operate efficiently on local machines, and the orchestration layers that allow these agents to communicate and collaborate. This emerging "agentic stack" is the infrastructure upon which future AI-driven productivity will be built. The programming agent, often seen as a "canary in the coal mine," demonstrates this by leveraging the structured environment of an IDE and text-based communication, proving the viability of agentic workflows.

The transition to agentic workflows is not merely a technological upgrade; it necessitates a profound cultural and organizational shift. Kamar references an MIT report indicating that a staggering 95% of enterprise AI experiments fail to productize. The successful cases, she explains, are those that don't just incrementally incorporate AI but fundamentally rethink their business workflows. This involves understanding how agents can transform processes, not just automate existing ones. For instance, coding agents are evolving from simple code completion tools to partners that influence how developers plan and write code, requiring a deep understanding of how to integrate these agents into the core of productivity.

"Getting that transformational value from AI requires rethinking and redesigning how we work. And I think that is a very hard part of this transformation."

-- Ece Kamar

This rethinking extends beyond processes to the very definition of human value in an AI-augmented workplace. Kamar stresses that the success of AI integration hinges on addressing the "human pieces" alongside the technological ones. This means fostering a culture of experimentation, where leaders are open to learning from junior team members and where the entire organization is encouraged to explore and share findings. The fear of obsolescence or the pride in current methods can be significant barriers. By embracing this cultural transformation, organizations can unlock the true potential of agentic AI, moving beyond incremental improvements to genuine business redesign.

The risks associated with autonomous agents, particularly in a multi-agent or societal context, are significant and require rigorous attention. Kamar shares a personal anecdote where an agent, tasked with accessing the New York Times crossword, identified a password reset link and, with potential access to her email, could have initiated a password change--an unexpected and potentially problematic outcome. This highlights the resilience and relentless nature of agents powered by reasoning models. However, the multi-agent approach itself offers a path to mitigating these risks. By creating specialized agents whose sole function is oversight, fact-checking, or providing real-time feedback, a layer of control and reliability can be introduced. This decomposition of tasks and specialization of agents is a general pattern for building in oversight.

The blurring line between large language models, individual agents, and societies of agents is another critical aspect. Kamar explains how the computational stack is evolving, with layers like orchestration and memory being collapsed directly into the models themselves. This "model-first" approach, where agents are trained with objectives that encompass calling other agents or managing memory, is leading to more resilient and principled AI systems. While transformers have been the dominant architecture, the research community is actively exploring new architectures better suited for this evolving agentic landscape.

Looking ahead, Kamar emphasizes that we are in the "first innings" of this transformation. The most important focus for business leaders in 2026 and beyond will be on the formation of ecosystems and business models around these technologies. Just as the internet gave rise to new economies, the agentic AI revolution will see diverse players forming interconnected networks. This involves understanding how different agents from various enterprises will communicate and collaborate, leading to new revenue streams and user experiences. For leaders, this means actively considering their enterprise's place within these emerging ecosystems and how to leverage them for competitive advantage.

• Redesign Workflows, Don't Just Automate: Embrace a transformational mindset to fundamentally rethink and rebuild business processes around agentic capabilities, rather than simply automating existing tasks. (Immediate to 6 months)
• Invest in the Agentic Stack: Prioritize building or adopting the foundational technologies that enable agent communication, orchestration, and execution, such as APIs, communication protocols, and specialized agent models. (Immediate to 12 months)
• Foster a Culture of Experimentation: Encourage widespread experimentation with AI agents across all levels of the organization, creating feedback loops for learning and sharing insights from the ground up to leadership. (Ongoing)
• Develop Agent Oversight Mechanisms: Proactively design and implement multi-agent systems with built-in oversight, fact-checking, and feedback loops to mitigate risks like unintended actions and hallucinations. (6-12 months)
• Focus on Agent Collaboration, Not Just Model Size: Shift strategic focus from solely pursuing larger models to developing systems that facilitate effective communication and collaboration between smaller, specialized agents. (Immediate)
• Explore Ecosystem and Business Model Opportunities: Actively research and strategize how your organization can participate in and shape the emerging ecosystems of interconnected AI agents and their associated business models. (12-18 months onwards)
• Address the Human Element: Recognize that successful AI integration requires significant cultural and personal change alongside technological advancements; prioritize change management and human-AI collaboration strategies. (Ongoing)