Autonomy depends on more than a good planner. It also depends on memory, tools, and the glue that keeps the system from diverging or hallucinating when it must act in the real world.
Modern agent stacks combine a planning module with a working memory and tool interface. These pieces must be coordinated through a shared control loop, or the agent becomes a loose collection of predictions instead of a coherent decision-maker.
1. Planning the right next action
Planning is the agent's horizon. It turns goals into steps, selects the highest-value moves, and adapts when the environment changes. The difference between a planner and a planner-plus-memory is whether the agent can recover from unexpected outcomes.
2. Memory for context and state
Memory gives the agent a persistent record of observations, goals, and previous actions. Without it, each decision is based on a short-term snapshot, making longer workflows brittle and error-prone.
3. Tool use and ecosystem integration
Tool access is where agents become useful. Controlled tool execution allows the system to act beyond language, but it also introduces a safety surface that requires strict validation and fallback behavior.
4. What still breaks
Today’s agents still fail on planning depth, hallucinated tool calls, and conflicting objectives. The stack is only as strong as its weakest integration point, and those weak links are often in the handoff between planner and executor.