Building Agents

In the rush to deploy AI-powered agents, it’s easy to mistake complexity for capability. The most effective agentic systems don’t emerge from intricate frameworks but from well-structured, composable patterns. The key to building lasting, maintainable agents is standardization—without it, agents risk becoming the next legacy maintenance nightmare.

After our last post on Agents Rising, we wanted to go a little but deeper on the science and art of agent building.

Defining Agents and Workflows

The term agent is often used interchangeably with AI-powered automation, but distinctions matter. Many of the leading model builders separate agentic systems into two broad categories:

Workflows: LLMs and tools orchestrated through predefined code paths. Predictable, consistent, and well-suited for structured tasks.

Agents: Dynamic, self-directed systems that determine their own processes, tool use, and execution paths.

Workflows optimize reliability. Agents optimize flexibility. Choosing between them isn’t about technological preference—it’s about balancing cost, latency, and task complexity. Most businesses will find that simple LLM calls with in-context retrieval suffice. When more adaptability is required, well-designed agents step in.

Overengineering

Many AI frameworks introduce unnecessary layers of abstraction. This often obscures prompts and responses, making debugging harder and adding brittle complexity. The best approach? Start with direct API calls, keeping implementations lean. If a framework is necessary, ensure you understand its inner workings—incorrect assumptions lead to costly errors.

For those interested in a deeper read on the concepts and code for agent frameworks, this post from Hugging Face is a pretty good place to start.

AI Agents in Action

Agents are emerging in production as LLMs mature in reasoning, planning, tool usage, and error recovery. They engage with users through commands, conversations, events, or notifications. Consider customer support:

AI agents can combine chat-based interactions with database lookups, enabling instant access to customer history.

They can take real-world actions—issuing refunds, updating orders, or modifying tickets.

Success can be measured with precision (e.g., resolution rates, reduced handle times).

Businesses that structure agent interactions carefully are seeing results, with usage-based pricing models demonstrating confidence in AI-driven resolutions.

The Role of Model Context Protocol (MCP)

Anthropic’s Model Context Protocol (MCP) introduces a standard for AI applications to connect with external tools and data. MCP enables:

Tool discovery: Agents dynamically identify available resources.

Tool invocation: AI models execute precise actions within real-world contexts.

However, deploying MCP servers remains a challenge—cross-platform consistency, environment conflicts, and complex dependencies slow adoption. Solutions like Docker help containerize these environments, ensuring seamless deployment and maintenance.

Why Standardization Matters

Agents are powerful because they integrate LLM outputs into workflows, allowing AI to influence execution dynamically. But without clear standards, each implementation risks becoming an isolated, hard-to-maintain system. The lesson?

Keep architectures modular.

Use standardized protocols.

Ensure tool interoperability.

AI-driven agents are not just a collection of functions, prompts, and API calls—they require structured governance. Businesses that prioritize agent standardization today will avoid the maintenance burdens of tomorrow.