Lou Bichard on Agent Swarms and the Missing Primitive

Lou Bichard of Ona discusses the challenges of agent swarms, the missing coordination primitive, and the future of software factories powered by AI agents.

7 min read
Lou Bichard presenting on Agent Swarms at AI Engineer Europe
Lou Bichard, Field CTO at Ona, discusses the critical role of coordination in agent swarms.· AI Engineer

Lou Bichard, Field CTO at Ona, presented "The Missing Primitive for Agent Swarms" at AI Engineer Europe. Bichard discussed the challenges and solutions in building software factories powered by AI agents. He highlighted the need for better coordination mechanisms to manage the complex interactions and workflows inherent in agent swarms.

Lou Bichard on Agent Swarms and the Missing Primitive - AI Engineer
Lou Bichard on Agent Swarms and the Missing Primitive — from AI Engineer

Visual TL;DR. Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive. Missing Primitive needs State Machines. Missing Primitive future CLI Tools. Missing Primitive solves Improved Orchestration.

  1. Software Factories Vision: automating software development lifecycle incrementally
  2. AI Agent Swarms: multiple coding agents working together on tasks
  3. Coordination Problem: complex interactions and workflows between agents
  4. Missing Primitive: lack of a fundamental mechanism for agent coordination
  5. State Machines: potential solution for structured agent communication
  6. CLI Tools: future development for managing agent swarms
  7. Improved Orchestration: enabling proactive work without direct human engagement
Visual TL;DR
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive uses creates due to Software Factories Vision AI Agent Swarms Coordination Problem Missing Primitive From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive uses creates due to SoftwareFactories Vision AI Agent Swarms CoordinationProblem Missing Primitive From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive uses creates due to Software Factories Vision automating software development lifecycleincrementally AI Agent Swarms multiple coding agents working together ontasks Coordination Problem complex interactions and workflows betweenagents Missing Primitive lack of a fundamental mechanism for agentcoordination From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive uses creates due to SoftwareFactories Vision automating softwaredevelopmentlifecycle… AI Agent Swarms multiple codingagents workingtogether on tasks CoordinationProblem complexinteractions andworkflows between… Missing Primitive lack of afundamentalmechanism for agent… From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive. Missing Primitive needs State Machines. Missing Primitive future CLI Tools. Missing Primitive solves Improved Orchestration uses creates due to needs future solves Software Factories Vision automating software development lifecycleincrementally AI Agent Swarms multiple coding agents working together ontasks Coordination Problem complex interactions and workflows betweenagents Missing Primitive lack of a fundamental mechanism for agentcoordination State Machines potential solution for structured agentcommunication CLI Tools future development for managing agentswarms Improved Orchestration enabling proactive work without directhuman engagement From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Software Factories Vision uses AI Agent Swarms. AI Agent Swarms creates Coordination Problem. Coordination Problem due to Missing Primitive. Missing Primitive needs State Machines. Missing Primitive future CLI Tools. Missing Primitive solves Improved Orchestration uses creates due to needs future solves SoftwareFactories Vision automating softwaredevelopmentlifecycle… AI Agent Swarms multiple codingagents workingtogether on tasks CoordinationProblem complexinteractions andworkflows between… Missing Primitive lack of afundamentalmechanism for agent… State Machines potential solutionfor structuredagent communication CLI Tools future developmentfor managing agentswarms ImprovedOrchestration enabling proactivework without directhuman engagement From startuphub.ai · The publishers behind this format

Bichard began by framing the current industry trend towards building "software factories." He defined this as a commitment to incrementally moving human involvement out of the loop across the entire software development lifecycle (SDLC). The goal is for work to proactively happen when humans are not directly engaged.

The Vision for Software Factories

The concept of a software factory envisions a highly automated process for software development. Bichard noted that many companies are exploring ways to leverage coding agents and apply them across the SDLC. This involves not just individual agents but also the orchestration of multiple agents working in concert.

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He illustrated this with examples of how agents can be used for various tasks, from planning and coding to review and deployment. The goal is to create a system where agents can autonomously handle complex tasks, minimizing the need for human intervention at every step.

Background Agents and Their Roles

Bichard introduced the concept of "background agents," which he described as autonomous entities that can perform tasks within an organization's infrastructure. He showcased different patterns of agent behavior: swarms where agents converge on a single result, fleets where agents work in parallel across repos, event-driven agents triggered by specific events, and scheduled agents for routine tasks.

He emphasized that the platform Ona is building is designed to facilitate these background agents. The platform provides agents with isolated development environments, allowing them to execute tasks without interfering with each other or the broader system. This isolation is key for ensuring reliability and reproducibility.

The Coordination Problem

A significant challenge in building effective agent swarms is the lack of a robust coordination layer. Bichard pointed out that while tools like GitHub are essential for code management, they are not designed to handle the complex interdependencies and state management required for coordinating multiple autonomous agents.

He explained that GitHub's pull request system, while useful for human collaboration, becomes overly noisy and difficult to manage when dealing with numerous automated agent interactions. The sheer volume of automated tasks, potential conflicts, and the need for precise state tracking makes existing tools inadequate.

What is Missing: Coordination Primitives

Bichard identified coordination as the "missing primitive" in the current landscape of agent swarms. He elaborated on the four key primitives needed for effective agent systems: runtimes (which are largely solved), orchestration (partially solved), triggers (solved), and coordination (missing).

He stressed that while individual agents and their execution environments (runtimes) are becoming more mature, the ability for these agents to effectively collaborate, communicate, and manage shared context remains a significant hurdle. The industry is still in the early stages of building solutions for this critical aspect of agent swarms.

The Future: State Machines and CLI

Looking ahead, Bichard suggested that state machines are a promising primitive for agent workflows, allowing agents to work with explicit states and transitions. This approach provides a more structured and manageable way to define agent behavior and interactions.

He also highlighted the importance of packaging these workflows as CLI tools. This allows for greater composability, scriptability, and version control, making it easier to manage and deploy complex agent systems. The goal is to create an environment where agents can operate autonomously and reliably, contributing to a more automated software factory.

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