Multi-step reasoning
Multi-step reasoning is when an agent decomposes a problem into a sequence of steps, executes each one (often using tools), and feeds the results back into its next step. The result is action that requires more than one move.
Why it matters
A single-shot question-and-answer loop is enough for "what is the capital of France." It is not enough for "look up my last invoice for this customer, see what they paid, draft a follow-up email, send it after I approve." That request needs four distinct moves, each one informed by the previous.
Multi-step reasoning is what makes an agent useful for operational work. The agent reads the request, plans the steps, calls the right tool first, reads the result, plans the next step, calls the next tool, and so on until the goal is reached. Each step writes to context; the agent keeps state about what has been done.
The engineering challenge is bounding the loop. Without budget caps, an agent can loop forever, calling tool after tool, accumulating context, and either timing out or producing nonsense. Good agent runtimes set per-turn budgets, recognize when a step has failed, and fall back gracefully. The user experience improvement is dramatic when this is well-handled and dramatic in the other direction when it is not.
How Ajeris handles this
Ajeris's agent loop runs multi-step reasoning with per-turn budgets to bound cost and latency. Tool failures bubble back into the agent's context, allowing the agent to retry, fall back, or surface the failure to the user. For consequential actions, an approval parser holds the action in queue until the user replies "do 1" or similar to authorize.
