AI Agents: Planning, Tools and Memory

An agent uses observations to choose actions toward a goal. A chatbot answer alone is not proof that the requested work happened; an agent must connect its output to tool results and a verifiable outcome.

#The main components

Planning orders steps and revises them when evidence changes. Tools read or change an external system. Memory retains relevant task state and knowledge. The model is the decision layer, while the application enforces permissions and executes approved operations.

A ReAct-style loop alternates action and observation. The useful application record is what was attempted, what happened and why the next step is necessary; it need not expose hidden reasoning.

#A concrete customer-support task

For an order question, retrieve the caller's authorized order record, report the actual status and offer the allowed next action. Reading an order and submitting a return are different capabilities. Do not give a read-only assistant write access merely because the user asks a broad question.

#Google Cloud tools

Google Cloud's agent-building services can provide managed integrations and operational features. Check current product documentation for identity, region, connectors and deployment support. Do not assume every agent is grounded automatically or deployable to every chat platform with one click.

#Evaluate the result

Test an ordinary request, a missing record, a tool timeout and an unauthorized write. Bound steps, execution time and usage. Confirm that a successful final answer corresponds to the actual external state.

For a local learning exercise, use fictional records and deterministic tool results. Move to real services only with explicit authorization and verified access boundaries.

Agent engineering handbook · Google Cloud agent documentation↗