@GarryTan, President of Y Combinator, recently delivered a compelling vision for the future of work in AI-native organizations. He explains agentic workflows using a straightforward and powerful analogy: treat AI agents like human employees in a growing company. You hire them, train them, manage them, and equip them with the tools and knowledge they need to succeed.
This framework moves us away from one-off prompting toward building scalable, institutional intelligence. Here is how the key components map to a traditional workforce.
AI Agent = Employee Each agent is a specialized team member focused on executing particular tasks or responsibilities. Instead of writing software in the traditional sense, you are hiring, training, and directing a workforce made of markdown and code.
Skill File = Job Description + Process Manual A skill file clearly defines one specific capability. It serves as both the job posting and the detailed operating manual that tells the agent exactly how to perform that role consistently and effectively. Write it well once, and you avoid repeating the same instructions.
Resolver Table = Org Chart / Task Router This component handles routing. When a new task arrives, the resolver determines which agent is best suited to handle it, much like a manager assigning work across departments.
Trigger Evals = Performance Reviews These are the checks that verify whether a skill is performing correctly. They act as ongoing evaluations to maintain quality and alignment with expectations.
Company Brain / Memory System = Library + Librarian This central knowledge repository supplies the right context at the right time. It turns individual agents from basic assistants into knowledgeable colleagues who understand the broader organizational picture.
We’ve added one more…
Reliability Hardening = Structured Mentoring The veteran mentor who provides institutional wisdom and processes to get things done across different departments. It guides agents on how to execute their jobs successfully while avoiding landmines, working efficiently, and scaling reliably.
Think of it as the experienced guide who makes sure every employee has the practical know-how to deliver results without costly mistakes.
Trustabl.ai introduced reliability hardening to agent development to provide the context, and configuration needed to operate reliably and successfully execute in production. Our open-source tool scans your entire agent repo for issues that result in failure. According to academics and experts, this is a big problem for agents. It’s also an excellent extension of Garry’s analogy.
Just like the experienced elder stateman guiding the noobs on how things really work in the organization, and how to get things done, Trustabl informs your agents how to succeed. The result is simple: less failure, more success, and reduced token waste.
The real leverage in AI is not just in larger models. It comes from how you structure and harden the work itself. By treating agents as employees and investing in proper training, routing, evaluation, memory, and mentorship, companies build systems that improve daily rather than repeating errors.
With Trustabl.ai, you move quickly from experimental agents to hardened, production-ready systems that support compliance, observability, and sustainable scale.
We are entering an era where AI-native organizations win through disciplined execution and reliability. The founders and teams that master this workforce model will outpace everyone else.
If you are building with AI agents, we would love to explore how Trustabl.ai can strengthen your setup. Contact us to discuss turning your agent workforce into a reliable, high-performing team.
