AI Agents: 7 Building Blocks to Master Beyond Model Selection

Beyond the model, the design of an AI agent's framework revolves around seven components and their breaking points. Production risks, execution protections, and observability play a central role. As the power of models converges, the focus shifts to an environment that limits what the agent sees, does, and retains, as well as how to validate its actions.

Recurring Failures and Safeguards to Prevent Them

Several typical failures are linked to the architecture of agents: context degradation, tool overload, fragile tool connections, insufficient verification, and missing safeguards. To address these issues, the framework includes safeguards designed to prevent dangerous actions and structured observability, with logs, traces, and self-verification, to monitor and control behavior during execution.

Seven Components to Structure an Operational Agent

Seven essential building blocks are proposed: the system prompt, tools, and a workspace that combines a sandbox and files. Additionally, there is memory with context management and, if necessary, persistent memory, the reason-action-observation loop, safeguards against risky actions, and observability based on logs, traces, and self-verification.

Beyond the Model: The Role of the Framework and Underlying Trends

An agent's capability is not solely dependent on the model: the model provides reasoning, while the framework makes action possible. A distinction is made between frameworks, seen as foundational elements, and frameworks designed as protections and controls during execution. With the convergence of raw capabilities, the engineering effort shifts towards designing an environment that limits what the model can see, do, and retain, as well as how to verify success and failure, even as attention often initially focuses on model selection.