Combining tools, memory, and reasoning
Earlier in this section, we saw that the Agents SDK can manage workflows that span multiple steps. This lesson exists to show how those workflows stay coherent when tools, memory, and reasoning are all involved at once. In real agents, these pieces are never isolated. They constantly inform and constrain each other as work progresses.
Coordinating tool usage with agent memory
In an SDK-managed agent, tools do not operate in a vacuum. Each tool invocation happens in the context of what the agent already knows. The SDK makes recent conversation state and persisted memory available so tool choices are grounded in prior steps rather than made blindly.
When an agent selects a tool, it is usually doing so because memory indicates that the tool is relevant right now. For example, an agent generating pages might remember which planets have already been processed and choose a tool only for those still missing.
def render_planet_page(planet_name: str) -> str:
return f"<h1>{planet_name}</h1>"The important point is not the tool itself, but that its use is informed by remembered state.
Using memory to inform reasoning decisions
Reasoning steps in the SDK rely heavily on memory. The agent’s reasoning is shaped by what has already happened, what succeeded, and what remains incomplete. This prevents each step from starting from scratch.
Memory can include prior decisions, partial results, or high-level goals. When the agent reasons about what to do next, it does so with this accumulated context in mind. That context helps the agent avoid repeating work or contradicting earlier actions.
In practice, this means reasoning becomes incremental. Each decision builds on the last instead of replacing it.
Updating memory based on tool outcomes
After a tool runs, its result is not just returned and forgotten. The SDK allows tool outcomes to be reflected back into agent memory. This update step is what lets future reasoning remain consistent with reality.
If a tool successfully generates a page, that success can be recorded. If it fails, that outcome can also become part of memory. Either way, subsequent steps reason with an updated picture of the world.
result = render_planet_page("Mars")What matters is that the agent now has new information it did not have before, and that information persists across steps.
Maintaining coherence across workflow steps
Coherence is the result of tools, memory, and reasoning staying aligned. Tools act, memory records, and reasoning decides based on what memory contains. The SDK’s workflow support ensures these transitions happen in a controlled order.
This structure prevents common problems like reasoning based on outdated assumptions or tools being invoked without awareness of prior results. Each step has a clear before-and-after state, even when the workflow spans many turns.
As workflows grow longer, this coherence becomes more important than the individual intelligence of any single step.
Designing robust SDK-managed workflows
Robust workflows are designed with the interaction between tools, memory, and reasoning in mind from the start. Tools should return results that are easy to store. Memory should capture just enough to guide future decisions. Reasoning should assume that memory is authoritative.
The SDK encourages this separation of responsibilities while still keeping them tightly coordinated. When designed well, workflows remain understandable even as they become more capable.
This design discipline is what allows SDK-managed agents to scale beyond simple demonstrations.
Conclusion
At this point, we are oriented to how tools, memory, and reasoning work together inside an SDK-managed workflow. We have seen how memory informs decisions, how tools update memory, and how the SDK maintains coherence across steps. With this mental model in place, it becomes easier to design workflows that remain reliable as they grow more complex.