Creating Your First Agents
Core Concepts
Understanding the fundamental distinction between AI tools and AI agents is critical before you begin building. A simple LLM is a powerful talker, while an Agent is a strategic doer. This distinction shapes everything about how you approach agent creation, deployment, and management.
Agents perform complex decision-making, reasoning, planning, and real-world actions through tool integration. They don't just respond to queries—they pursue objectives, navigate obstacles, and adapt their approach based on feedback and changing circumstances.
The journey from conversational AI to true agency involves understanding the progression of capabilities that transform a simple question-answering system into an autonomous problem-solver. Each level builds upon the previous, adding new dimensions of intelligence and autonomy.
The difference between a tool and an agent isn't just about capability—it's about intention. Tools wait for instructions; agents pursue goals. This fundamental shift in orientation changes everything about how you design, deploy, and interact with AI systems.
Agent Progression Levels
Agents exist on a spectrum of capability and sophistication. Understanding this progression helps you identify where to start and how to evolve your agents over time.
Level 0: Null Agent
Basic prompt-response interaction with no external tools or memory. This is a simple LLM interface—useful for quick queries but incapable of sustained work or interaction with external systems. The agent forgets everything after each conversation and cannot take any actions beyond generating text.
Level 1: Grounded Context
Agents connected to verified organizational knowledge bases. These agents can access your company's documentation, policies, and data to provide accurate, contextual responses. They understand your specific domain but still operate reactively, waiting for questions rather than proactively identifying opportunities.
Level 2: Action and Reason
Agents equipped with callable corporate functions and APIs. These agents can not only retrieve information but take actions—sending emails, updating databases, scheduling meetings, or triggering workflows. They reason about which tools to use and in what sequence to accomplish tasks.
Level 3: Agentic Loop
Self-correcting iterative process following the Goal, Plan, Execute, Observe, Refine cycle. These agents don't just execute once—they evaluate their results, identify shortcomings, and iterate until objectives are met. This is where true autonomy begins.
Enterprise Readiness Requirements
Personal agents built for individual productivity have different requirements than agents deployed across teams or organizations. Shareable agents require rigorous preparation across multiple dimensions to ensure they're safe, effective, and maintainable.
Minimum Viability
Every shareable agent must have a clear name that communicates its purpose, a detailed description of what it does and doesn't do, and an assigned owner responsible for its maintenance and improvement. Without these basics, agents proliferate without accountability, creating confusion and risk.
Governance
Enterprise agents require defined maintenance cycles—scheduled reviews to ensure they remain accurate and effective as your business evolves. They need role-based access control to ensure only appropriate personnel can use or modify them. And critically, they require a centralized kill switch—the ability to immediately disable an agent if it malfunctions or causes harm.
Observability
You can't improve what you can't measure. Enterprise agents need comprehensive usage monitoring to understand who uses them, how often, and for what purposes. They need efficacy tracking to measure whether they're actually solving the problems they're designed to address. And they need cost management to ensure the value they provide exceeds their operational expenses.
Compliance
Before deployment, agents should undergo security reviews to identify potential vulnerabilities. They need performance baselining to establish expected behavior patterns that make anomalies detectable. And they require official registration in your organization's agent inventory for audit and governance purposes.
The Build Hierarchy
Not all agents need to be built to the same standard. The appropriate level of rigor depends on who uses the agent, what risks it poses, and how broadly it's deployed. Understanding this hierarchy helps you allocate resources appropriately.
Tier 1: Individual Personal Agents
These are agents built by individuals for their own use—personal productivity tools that never touch shared systems or sensitive data. Low-code solutions are ideal here. The builder is also the user, so the feedback loop is immediate. If something breaks, only you are affected. These agents can be experimental, iterative, and informal.
Tier 2: Departmental Agents
These agents serve shared processes within a team or department. They require more formal design because multiple people depend on them. Documentation becomes important—others need to understand how to use the agent and what to expect. Testing should be more rigorous, and there should be a clear owner responsible for maintenance. However, the blast radius of failure is still contained within the department.
Tier 3: Enterprise Agents
These agents operate across organizational boundaries, touch critical systems, or handle sensitive data. The Agentic Services Group—a specialized team with deep expertise in agent development and governance—should handle infrastructure and high-risk initiatives at this level. Full compliance, security review, and formal governance are non-negotiable. The consequences of failure at this tier can be severe and far-reaching.
Match your investment in agent development to the risk and scale of deployment. Over-engineering personal agents wastes time; under-engineering enterprise agents creates catastrophic risk.
Strategic Sourcing: Build, Buy, or Customize
Not every agent needs to be built from scratch. Strategic sourcing decisions determine where to invest your development resources for maximum impact. The framework is straightforward: build for proprietary competitive advantage, buy commodity solutions, and customize vendor products for integrated enterprise deployment.
Build: Proprietary Competitive Advantage
When an agent embodies unique intellectual property, proprietary processes, or competitive secrets, build it yourself. These are the agents that differentiate your organization—the ones competitors can't replicate by purchasing off-the-shelf solutions. Building requires more resources but provides more control and can create lasting competitive moats.
Buy: Commodity Solutions
For standard business processes where your needs match industry norms, purchasing established solutions is usually wiser. Why build a generic customer service bot when dozens of mature products exist? Buying gets you to value faster, with the added benefit of vendor-maintained updates and support. The key question: does differentiation in this area actually matter to your business?
Customize: Integrated Enterprise Deployment
Often the best path lies between building and buying. You purchase a capable base product and customize it with your organization's specific knowledge, processes, and integrations. This approach combines vendor expertise in core capabilities with your domain expertise in application. It's particularly effective when you need enterprise-grade infrastructure but want agents that understand your unique business context.
The biggest mistake organizations make is building when they should buy—wasting months recreating commodity functionality that could be purchased in days. The second biggest mistake is buying when they should build—surrendering potential competitive advantage for the convenience of a vendor solution.
The Innovation Model: Ephemeral Software
Traditional software development assumes longevity. You build applications expecting them to run for years, justifying significant upfront investment in architecture, documentation, and testing. This assumption increasingly fails in the agentic era.
The economics have changed. When creation costs drop dramatically, the calculus of software development inverts. Instead of building permanent solutions to anticipated problems, you can build temporary solutions to immediate problems—and rebuild when circumstances change.
What is Ephemeral Software?
Ephemeral software consists of temporary solutions built quickly to solve immediate problems. These agents aren't designed for permanence—they're designed for utility. Build them fast, use them while they're useful, and discard them without regret when the situation evolves.
This reflects a fundamental shift in how we think about software value. Permanent software amortizes development costs over years of use. Ephemeral software justifies its existence if it solves today's problem, even if it's obsolete tomorrow. The question isn't "will this still be useful in three years?" but "does this solve the problem I have right now?"
Implications for Agent Development
This model encourages experimentation. When building is cheap and fast, you can try multiple approaches and see what works. You can build agents for one-time tasks without guilt about "wasted" development effort. You can iterate rapidly, replacing agents entirely rather than painstakingly maintaining them.
The danger is losing valuable patterns and learnings when ephemeral agents are discarded. Smart organizations capture the insights from temporary solutions even when the solutions themselves are abandoned.
Getting Started: Your First Agent
Theory matters, but practice matters more. Here's how to begin your journey into agent creation with a structured approach that balances learning with immediate value.
Start with a Real Problem
Don't build an agent to learn agent-building. Build an agent to solve a problem that actually bothers you. The motivation of genuine frustration keeps you engaged through the inevitable challenges. Pick something you do repeatedly, something that takes time, something where mistakes have real consequences.
Define Success Clearly
Before building, articulate what success looks like. How will you know if the agent is working? What would it need to do to save you time or reduce errors? Having clear criteria prevents scope creep and provides motivation as you see the agent approach your goals.
Start at Level 0 and Climb
Begin with the simplest possible agent—a basic prompt that addresses your problem. Test it extensively. Understand its limitations. Only then add complexity: connect it to knowledge bases (Level 1), give it tools (Level 2), enable iteration (Level 3). Each level should demonstrate clear value improvement before you advance.
Document as You Go
Record what works, what doesn't, and why. Future you—and future colleagues—will thank you. This documentation becomes the foundation for scaling your agent practice from personal productivity to team-wide transformation.
The best way to learn agent development is to build agents that solve real problems. Start small, iterate quickly, and let genuine need drive your learning.
From First Agent to Agent Practice
Creating your first agent is just the beginning. The goal isn't a single successful agent—it's building the capability to create, deploy, and manage agents as a core organizational competency.
This chapter has provided the conceptual foundation: understanding agent progression levels, enterprise readiness requirements, build hierarchy, strategic sourcing, and the ephemeral software model. These frameworks help you make better decisions as you scale from individual experiments to organizational transformation.
Key Takeaways
Agents are different from tools. They pursue goals rather than wait for instructions. This fundamental distinction shapes everything about how you design and deploy them.
Match rigor to risk. Personal agents can be informal experiments; enterprise agents require formal governance. Over-engineering wastes resources; under-engineering creates danger.
Strategic sourcing matters. Build for competitive advantage, buy commodity solutions, customize for integration. Making the wrong choice wastes either time or opportunity.
Embrace ephemeral software. When creation is cheap, permanence becomes optional. Build for today's problem without guilt about tomorrow's obsolescence.
The next chapter explores how to move from creating individual agents to commanding fleets of them—scaling your agent practice from personal productivity to organizational capability.
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