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AI Agents & Autonomous Systems

Agent Memory and Planning

Design agent memory, task decomposition, planning, reflection, and state without creating unsafe or confused agents

30 min read· agents· memory· planning· reflection

Agent Memory and Planning

Better agents are not just bigger prompts. They need state, memory, planning, tools, and stopping rules.

Memory types

MemoryPurposeExample
Working memorycurrent task statechecklist for today's run
Episodic memoryprior interactionswhat happened in last support case
Semantic memorydurable factsuser's stable preference
Procedural memoryhow to do workrun tests before finishing

Memory should be intentional. Storing everything creates privacy risk and confusion.

Planning patterns

PatternUse when
ReActtool use and observation loops
Plan-and-executemulti-step tasks with clear subtasks
Reflectionagent needs to critique or repair its work
Tree searchmultiple possible solution paths
Supervisor/subagentsspecialized workers are useful

Good plans are testable

A strong agent plan includes:

  • objective
  • constraints
  • required inputs
  • tools needed
  • steps
  • success criteria
  • stopping condition
  • escalation rule

Reflection without loops

Reflection helps agents catch mistakes, but it can loop forever.

Use:

  • max iterations
  • budget limits
  • changed-output requirement
  • external tests
  • human approval after repeated failure

Memory safety

Do not store:

  • secrets
  • credentials
  • regulated personal data
  • one-time user instructions
  • unverified facts
  • malicious instructions from documents

Store only stable, useful, permitted information.

Knowledge check

Q1: Why is "remember everything" a bad agent strategy?

It increases privacy risk, context noise, stale assumptions, and prompt-injection exposure.

Q2: What makes an agent plan testable?

Clear success criteria, stopping conditions, and verification steps.