Blog 1.3 AI Agent Basics: Building Blocks Part 1

Not long ago, if you wanted to automate a process, you cobbled together scripts, maybe some workflow software, and a few brave interns to monitor the process and alert when things broke. Agentic AI systems are the promise of faster and more reliable automation. But if you’ve ever seen what happens when technology races ahead of guardrails, you know the risks. The history of IT is littered with half-finished automation projects. An August 2025 report from MIT indicated that 95% of organizations have so far received “zero return despite enterprise investment of $30 billion to $40 billion into GenAI.” Organizations need to be able to build agentic AI systems that show significant return on investment, and that requires a tested process.

Here are the basic steps for building agentic AI with a focus on reliability, safety, and measurable ROI.

Step One: Define Goal and Scope

Before you open a single framework or vibe code your first agent, ask: what exactly am I building, and why? Imagine a sales leader who says, “I want an agent to generate and distribute weekly reports.” That is a clear goal without one, the project risks drifting into endless experimentation without measurable business value.

Once the goal is set, scope becomes the next discipline. What data sources will the agent draw from: CRM, ERP, or external market feeds? What formats are acceptable: Excel, PDF, dashboards? Who receives the report, and on what cadence? And equally important, who is responsible for the data quality and accuracy the agent depends on?

This is also the moment to define your non-negotiable guardrails. For example, the agent may be prohibited from including unrealized revenue in a report because doing so could misrepresent financial health. Or you may require that all reports above a certain dollar threshold receive human approval before distribution to senior leadership. These constraints are not there to slow the system down, they are there to keep trust intact. In agentic AI, guardrails are the safety rails that protect both the business and the people who rely on its output.

Step Two: Pick a Builder Framework

The next question is, how are you going to build this thing?

For many business leaders, the low code route is appealing. Tools like Microsoft Copilot Studio, Google AgentSpace, Salesforce Agentforce, or workflow platforms like n8n make it easy to get started. These tools are fantastic for prototypes or straightforward use cases, though you may eventually hit customization limits.

On the other end of the spectrum sits pro code. Frameworks like LangChain, LangGraph, CrewAI, OpenAI Agents, Amazon Bedrock AgentCore, Microsoft Azure Foundry, Google Vertex AI Agents, Cloudflare Agents offer deep flexibility but demand more technical skill. If your use case is nuanced, involves sensitive data, or will operate at scale, you may find yourself gravitating toward this camp.

Choose based on the skills of your team and the complexity of your requirements.

Step Three: Select the Core Intelligence

Every agentic system needs a “brain” for reasoning. That brain might be a single LLM (large language model), or it could be several working on different parts of the problem space. Here you face decisions about public versus private models. Free tier public models can expose data, like when what users thought were private ChatGPT conversations were shared with search engines.  And even the most advanced LLMs, like ChatGPT 5 still produce inaccurate or “hallucinated” responses at least 10% of the time. Privately hosted models models require more effort to maintain but give you more control.

Cost is another reality check. A model that charges fractions of a cent per query may sound cheap until your volume scales into millions of interactions. Compliance is also central. Has the model been tested for bias? Does it meet your organization’s ethical standards? Resources like the RiskRubric leaderboard can help you assess strengths and weaknesses before committing.

With the first three steps complete, you have the essential strategic decisions in place. In the next blog we’ll connect these foundations to the practical realities of data, tools, and operations.