Human Skills

When I instruct an agent to perform different tasks, I often expect it to understand more than what I explicitly write in the instructions. In many situations, I know what I want to achieve, but I do not know how to describe the problem completely, especially when I am dealing with a topic that is unfamiliar to me. This makes proactive collaboration one of the most interesting possibilities I see in agentic AI. If an agent can recognize the meaning behind incomplete instructions, identify important implicit cues, and help me turn an unclear intention into a more concrete task, the interaction could become much more efficient. ...

When I work with an AI agent on a complex task, I find that the precision of my instructions can have a significant effect on how smoothly the work proceeds. A vague instruction may lead to results that require several rounds of clarification, correction, and confirmation. A more precise instruction can give the agent a clearer understanding of what I expect from the beginning, which can save time for both the human and the AI. ...

Where should we draw the capability boundary between AI and humans? It may be tempting to assume that AI can never be as productive or innovative as a human, but this assumption is becoming increasingly difficult to maintain. Consider video generation models as an example. A carefully designed prompt or set of instructions is itself a product of human thinking, but the resulting video can sometimes be remarkably detailed, coherent, and visually innovative. ...

How much should we expect to remain unchanged in our work and learning as agentic AI develops? I think agentic AI is something we cannot simply avoid; we will have to accept it and learn how to cope with it. Its capabilities are not fixed, but continuously accumulated and improved over time. For people who are not directly involved in AI development, it is difficult to predict what these systems will be able to do in the near future. This makes the idea that “AI should never replace us” difficult to maintain as a general principle. ...

1. Working with Agentic AI: Blockchain Engineer How human skills and agentic skills are dynamically integrated and coordinated across blockchain engineering responsibilities 1.1. Blockchain Engineer as an Occupation A blockchain engineer designs, implements, and secures distributed ledger protocols, consensus mechanisms, and smart contracts to build scalable, trustless, and decentralized systems. This occupation connects cryptography, distributed systems theory, game theory, and software engineering by transforming theoretical trust models and economic incentives into robust, immutable, and executable network architectures. To ensure that these decentralized systems operate securely and efficiently without central oversight, the engineer must understand how cryptographic primitives, network latency, state machine replication, economic attack vectors, and scalability constraints collectively determine the viability and security of the blockchain ecosystem. ...

1. Working with Agentic AI: Computer Systems Analyst How human skills and agentic skills are dynamically integrated and coordinated across computer systems analysis responsibilities 1.1. Computer Systems Analyst as an Occupation A computer systems analyst studies an organization’s current computer systems, procedures, and business workflows to design technological improvements that enhance operational efficiency. This occupation serves as the critical bridge between business objectives and information technology (IT) implementation. Unlike software developers who primarily focus on writing application code, or business analysts who focus on high-level process optimization, the computer systems analyst evaluates the entire technological ecosystem—including hardware, software, networking, and data architecture—to design integrated solutions that solve complex data processing and operational problems. To ensure these systems deliver tangible value, the analyst must understand how business requirements, technical constraints, cost-benefit ratios, system compatibility, and user capabilities collectively determine the viability of an IT solution. ...

1. Working with Agentic AI: Document Management Specialist How human skills and agentic skills are dynamically integrated and coordinated across document management responsibilities 1.1. Document Management Specialist as an Occupation A document management specialist implements, configures, and administers enterprise-wide document management systems (DMS) and related procedures that allow organizations to capture, store, retrieve, share, and govern electronic content. This occupation connects information technology, business operations, and information governance by transforming chaotic, decentralized file shares into structured, secure, and highly searchable digital repositories. While a technical writer creates document content and a records manager defines long-term legal retention schedules, the document management specialist focuses on the active system architecture, metadata taxonomies, workflow automation, and daily operational efficiency of the enterprise content management (ECM) environment. To ensure that digital information remains accessible, secure, and compliant, the specialist must understand how business workflows, metadata standards, access control models, system integrations, and user behaviors collectively determine the viability and security of the organization’s information ecosystem. ...

1. Working with Agentic AI: Software Developer How human skills and agentic skills are dynamically integrated and coordinated across software development responsibilities 1.1. Software Developer as an Occupation A software developer researches, designs, codes, tests, and maintains computer software and applications to solve specific user needs and business problems. This occupation connects computer science, mathematical analysis, and engineering principles by transforming abstract requirements and logical models into functional, reliable, and scalable executable programs. While systems analysts focus primarily on high-level business processes and organizational architecture, and computer programmers focus strictly on translating provided specifications into code, the software developer owns a broader segment of the software development lifecycle (SDLC). To ensure that software solutions are robust and maintainable, the developer must understand how user requirements, algorithmic complexity, system constraints, security boundaries, and performance expectations collectively determine the viability and quality of the final application. ...