What actually makes an AI agent work? StackAI co-founder Bernard Aceituno broke it down in a recent Forbes piece, offering a practical framework for CIOs and enterprise leaders who want to move beyond the buzzword and understand what agentic AI looks like in practice.

The piece identifies five core pillars that separate a reliable, production-ready AI agent from a one-off demo.

The first is architecture: the underlying loop that lets an agent interpret a goal, plan steps, take action, observe the outcome, and self-correct. This is what makes multi-step automation possible without constant human hand-holding.

The second is profile and persona. The same LLM behaves very differently depending on how it's instructed. A well-defined persona keeps an agent grounded in its responsibilities, sets clear expectations for users, and makes it easier to catch and fix unexpected behavior.

The third is tool use. Text generation alone can't complete a business process. When agents are connected to the right systems, like CRMs, databases, mailboxes, and ticketing tools, they can actually create records, trigger workflows, and move work forward.

The fourth is evaluation and observability. Agents often take dozens of steps to complete a task, which means you need full visibility into what happened and why. Structured logs, tracing, and success metrics are what turn an experimental agent into one you can trust in production.

The fifth is memory and knowledge. Agents need access to reliable, up-to-date information to make good decisions. A strong memory layer, built on verified internal knowledge, is what keeps agents consistent and accurate over time.

Read the full article from Forbes here.

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