Planner-executor, reflection loops, verification steps, and agent evals — the patterns that make agents reliable in production.
Key vocabulary
Planner-executor pattern — one agent plans the steps, a separate agent executes them.
Reflection loop — the agent critiques its own output and iterates to improve it.
Verification step — a check that confirms the agent’s action had the intended effect.
Agent evals — systematic evaluation of agent performance across a set of test cases.
Speculative planning — generating a full plan before executing any step.
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The planner-executor pattern separates:
Planner-executor: the planner produces a structured plan; the executor carries it out. The plan is inspectable and correctable before execution begins.
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A reflection loop in an agent system allows the agent to:
Reflection loop = self-critique + iteration. The agent reviews its output (“What is wrong? What is missing?”) and produces a revised version. Improves quality at the cost of more LLM calls.
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A verification step in an agentic workflow:
Verification step: after the agent acts, it confirms the action succeeded. Example: after create_file(), call read_file() to verify the content. Without verification, agents can produce confident outputs based on silently failed actions.
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Agent evals are used to:
Agent evals = test suites for agent behaviour. Contain: (1) input scenarios, (2) expected outcomes or grading criteria, (3) a scoring function. Often use LLM-as-judge scoring because the expected output is a range of acceptable responses.
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When a team says “we use speculative planning before executing,” they mean:
Speculative planning: generate the complete step-by-step plan first, then execute. The plan is inspectable, prevents aimless tool calls, and gives the agent a clear commitment to work toward.