# The Agent Native Company Rick Blalock, Agentuity | AI Engineer World's Fair 2025 | 20:58 Source: https://www.youtube.com/watch?v=0ZPAvzhpGjw Channel: AI Engineer (https://www.youtube.com/@aiDotEngineer). Summarised by AIE Talks. Page: https://aietalks.com/talks/the-agent-native-company Published: 2025-06-03 Tags: engineering-culture, enterprise, team-adoption ## TL;DR - An agent-native company builds AI agents into its product, operations, and culture, so work depends on them rather than merely benefiting from occasional efficiency gains. - Employees spend more time directing agents, reviewing their output, and making higher-level decisions, which can produce flatter teams and different job titles. - AI fluency becomes a hiring requirement because employees need to guide agents, build workflows around them, and help other people use them effectively. ## Summary Rick Blalock describes an agent-native company as one built around AI agents from the start. Agents are part of the product, operations, and culture, and employees rely on them for routine work, coordination, prototypes, documentation, and customer-facing tasks. He contrasts this with an AI-enhanced business that would continue operating if its AI tools disappeared. In his example, a small team reviews agent work each morning, handles approvals, and manages several agents that produce code, content, documentation, and other deliverables. Blalock expects flatter organizations, fewer coordination layers, and roles that combine domain knowledge with AI fluency. He also argues that hiring and onboarding need to change. People must know how to guide agents, and new hires may spend their first weeks setting up systems for their jobs. His own team built an entire Aenta cloud infrastructure in a few weeks, which he presents as evidence of the operating model's speed and friction. ## Key ideas ### An agent-native company depends on agents for ordinary work [00:01](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=1s) Blalock defines an agent-native company as one built from the ground up with AI agents at the center of its product, operations, and culture. Agents are not occasional helpers or a feature added to an existing workflow. At Agentuity, team meetings include comments such as "just have Devon go do that," followed by a pull request before the standup ends. An agent writes the changelog and documentation after code is released. If those agents disappeared, employees would have to take back mundane work, costs would rise, productivity would fall, and the team would move more slowly. Blalock uses this dependence to separate an agent-native company from a business that simply uses AI here and there. ### People become conductors of a flatter human and AI organization [06:43](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=403s) In an agent-native company, employees spend more time coordinating work and making higher-level decisions while agents handle routine tasks and small decisions. Blalock says this changes the hiring profile and the shape of the organization. Middle-management layers can shrink because intelligent systems handle much of the coordination. His team can discuss a product idea in the morning, produce detailed requirements by the end of the day, and have agents already working on prototypes, copy, and documentation. He expects the organization chart to look less like a pyramid and more like a network of humans and AI. The point is a different operating model, rather than a slightly faster traditional company. ### Experimentation becomes easier when agents handle routine work [09:01](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=541s) Blalock connects agent-native work with a culture of rapid experiments and repeated refinement. Startup teams already talk about innovation and iteration, but agents make that process easier by taking care of routine work and helping produce prototypes. This gives people more time to focus on the parts that require judgment. He also says agents can improve through continued use. Blalock describes Cognition's Devin as having learned how his team handles certain tasks after documenting much of its code. For him, the combination of AI at the center, human orchestration, rapid experiments, and agents that learn over time produces a company that feels different from a traditional organization. ### The agent-native workday begins with reviewing what agents did [10:40](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=640s) Blalock's workday starts with a list of tasks and a review of agent activity. While walking or driving, he gives ChatGPT documents, links, conversations, and a Granola transcript to begin thinking through a larger question. By the time he reaches the task, some analysis is already available. Bugs and documentation issues placed in Linear may already have produced pull requests from Devin. Later in the day, he reviews pull requests, emails, and other material generated by agents. He also orchestrates content-marketing swarms that optimize copy and schedule social posts with Typefully. The human supplies direction and expertise while asynchronous agent work prepares material for review. ### AI fluency becomes part of the hiring bar [13:13](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=793s) Blalock argues that curiosity and adaptability are still valuable, but an agent-native company also needs people who can use AI well. He compares AI fluency with knowing how to use a word processor or keyboard for an office job. At Agentuity, a candidate who has not used AI raises an immediate warning flag, although Blalock allows that some people have not had the opportunity yet. Interviews need to test whether a person can guide agents, learn their techniques, and apply those tools to the work. This affects senior hiring too. A vice president's network and experience matter less if the person cannot use agents or bring in people who can. ### Onboarding includes building the agent systems for each job [16:48](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=1008s) If a company hires people to work in an agent-native structure, onboarding must include the systems that allow them to use agents effectively. Blalock says it may make sense to attach an engineer to a new team so that its agents are set up and working. A new hire could spend the first few weeks focused on configuring agents to perform the job. This changes onboarding from a process centered only on company knowledge and team relationships. It also makes the agent workflow part of the role itself. The company has to provide the tools and support needed for employees to direct their AI counterparts. ### Founders may need to discard parts of their previous operating experience [18:56](https://www.youtube.com/watch?v=0ZPAvzhpGjw&t=1136s) Blalock is direct about the difficulty for experienced founders and technology leaders. Years of experience can encourage people to repeat methods that fit an older way of working. He says some of that experience may no longer apply, so leaders need to examine it rather than assume it remains valid. Citing a recent PwC report, he says companies using AI only for small efficiency gains are falling behind companies that rebuild around it. His recommendation is to start from first principles and redesign processes, org charts, roles, and hiring criteria around human and agent teams. He describes this as painful and disruptive, but also as a possible advantage for companies willing to make the change. ## Notable quotes - "An agent native or an AI native company is a company built from the ground up with AI agents at the core of everything to augment human productivity and intelligence." (02:01) - "The morning time is check out what agents did, what they need to do, kick off the things that they need to do, and then come around around lunchtime review everything." (12:32) - "AI fluency becomes a must-have." (14:34) - "If you're only using AI for a small efficiency gain then you're falling behind." (19:55) - "Start from first principles, take this opportunity to step back, reimagine and refit your company and your culture for this future." (20:14) ## Tools & references mentioned - Agentuity - Devon - Cognition - Devin - ChatGPT - Granola - Linear - Typefully - PwC ## Who should watch - Founders deciding whether AI should be a feature, a set of productivity tools, or the basis for how the company operates. - Engineering and product leaders planning workflows in which employees review and direct agents throughout the day. - People responsible for hiring or onboarding who need to assess AI fluency and set up agent systems for new roles. ## Related talks - [Dispatch from the Future: Building an AI-Native Company](https://aietalks.com/talks/dispatch-from-the-future-building-an-ai-native-company) (Dan Shipper, Every, 17:58) - [From AI-Assisted to AI-Native: Building a Frontier Development Team](https://aietalks.com/talks/from-ai-assisted-to-ai-native-building-a-frontier-development-team) (Clare Liguori, AWS, 20:57) - [Agents for Everything Else](https://aietalks.com/talks/agents-for-everything-else) (swyx, AI Engineer, 14:10) - [Agentic Enterprise: What Your CEO Must Know About AI](https://aietalks.com/talks/agentic-enterprise-what-your-ceo-must-know-about-ai) (Hubert Misztela, Novartis, 28:04) - [The 4 Patterns of AI Native Development](https://aietalks.com/talks/the-4-patterns-of-ai-native-development) (Patrick Debois, 14:11)