7 Underrated Steps for Building a Scalable AI Agent

AI agents are popping up everywhere. They are getting more attention in the news feed, and their adoption rate is also increasing. AI agents are intelligent software systems that plan, work, and complete tasks autonomously, often with little to no human intervention. Hence, they are getting more mainstream attention from major companies.

Due to this massive surge in agent adoption and their use in everyday life, everyone is talking about building these smart assistants to handle tasks, analyze data, and even manage complex workflows.

This excitement is logical because the market for AI agents is expected to grow from over $5 billion in 2024 to over $216 billion by 2035, according to Roots Analysis. With the excitement building, many are realizing that taking an AI agent from a cool idea to a reliable, scalable system is trickier than it looks.

The mainstream buzz often is around the latest AI model, but here's a crucial insight: The secret isn't another bigger model—it's a clear structure that lets your agent think, remember, and act.

Let me explain…

The fundamental difference between a simple, all-in-one program and a true "agentic" system is in the agent's capacity to think through problems and effectively use tools to solve them. This ability to reason and act is what makes them so promising.

However, building these sophisticated, autonomous systems can feel like going through a maze without a map. To help you move faster and with more clarity, we've put together a straightforward roadmap based on a guide by Rakesh Gohel and Andreas Horn on LinkedIn, focusing on the 7 underrated steps for building a scalable AI Agent.

Here are the 7 underrated steps for building a scalable AI Agent:

1. Choose the right language model:

Pick the Large Language Model (LLM) that reasons instead of reciting. Look for support for chain-of-thought prompts and consistent outputs. Llama-3, Claude Opus, or Mistral-Medium are dependable first picks; open weights give you room to tweak temperature, context length, and safety filters.

2. Design the agent's reasoning loop:

Teach your agent how to think:

• Should it reflect before each answer?

• Does it plan a series of sub-tasks or start directly?

• When does it call an external tool?

• Start simple with ReAct or Plan-then-Execute templates, then improve once you see logs.

3. Write operating instructions that the model can't ignore:

Clearly define the rules your agent lives by and the style and tone of its responses. Spell out response formats (JSON, Markdown, plain text), tool-use rules, and tone.

• Treat this like an internal protocol document; small clarifications here prevent hours of prompt surgery later.

4. Add memory that lasts longer than the context window:

Large models can't recall prior chats once tokens scroll off the end. Patch that with:

• Using "sliding windows" to keep recent conversation parts in context for short-term memory.

• Creating summaries of older conversations to retain key information.

• Storing important facts, like user preferences, past decisions, or domain constraints made in previous interactions

• Toolkits such as MemGPT or the ZepAI can simplify the implementation of these memory features.

5. Wire up external tools and APIs

Reasoning is useful only if it drives actions.

• Fetch data from databases or websites.

• Update records in systems like CRMs.

• Perform calculations or run specific scripts.

• Tip: Consider using platforms like MCP (Model Context Protocol) to make connecting different tools and workflows easier with its plug-and-play features.

6. Give the agent a single, specific job

Vague instructions lead to poor performance. Be very clear about the agent's purpose.

• Good: "Summarize daily user feedback from the support channel and suggest three common improvement areas."

• Bad: "Be helpful and provide support."

Limit the scope of a job. Rather than detailing everything it should do, it's often more effective to focus your agent on what it should not do.

7. Scale from solo agent to multi-agent teams

Specialization beats bloat. One agent collects data, another interprets it, and a third formats the deliverable. As with single agents, limit the scope of each agent's job. Focus each agent on what not to do to maintain clarity in their roles.

Building a scalable AI agent depends on picking a reasoning-friendly model, teaching it how to think, defining its rules, adding memory, connecting tools, assigning a precise job, and, when the workload grows, promoting it to a small team. Following these 7 foundational steps can help you create intelligent and reliable systems, allowing you to turn your clever prototype into a dependable co-worker faster than you thought possible. It's less about a magic AI box and more about thoughtful engineering.