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Agentic AI Control Loop

See planning, tool use, memory, policy and evaluation as a controlled loop around the language model.

LLMsMCPTool CallingAgent MemoryGuardrailsEvaluationObservability

Agentic AI Control Loop

An agent is more than a prompt. Production agentic systems need explicit control over planning, tool authorization, state, retries, evaluation and termination conditions.

01

Tool access is a policy boundary, not a convenience feature.

02

Memory and state should be explicit architecture components.

03

Evaluation and stopping conditions prevent uncontrolled loops.

Agentic AI Control LoopSee planning, tool use, memory, policy and evaluation as a controlled loop around the language model.01User / SystemGoal02Agent RuntimeState + policy03Planner / ModelDecide next step04Tools / MCPExternal actions05MemoryContext + state06Guardrails + EvalCheck + stop
How to read it

Follow the handoffs, then ask where evidence exists.

01

User / System

Goal

02

Agent Runtime

State + policy

03

Planner / Model

Decide next step

04

Tools / MCP

External actions

05

Memory

Context + state

06

Guardrails + Eval

Check + stop

Architecture questions
Tool access is a policy boundary, not a convenience feature.
Memory and state should be explicit architecture components.
Evaluation and stopping conditions prevent uncontrolled loops.
Technology context
LLMsMCPTool CallingAgent MemoryGuardrailsEvaluationObservability

The diagram is intentionally architectural rather than vendor-specific. Use it as a mental model, then map the components to the actual environment.

Need the architecture applied?

Use the visual model as the starting point for a workshop or technical review.