01
Build context
Assemble instructions, messages, state and relevant retrieved data.
ImageFirm / Agentic AI Architecture
Agent Loop
How AI agents actually work.
An AI agent is not merely “an LLM that thinks repeatedly.” It is a software control loop that gives a model context, tools, memory, guardrails and stopping rules — then decides what happens next.
02
Call model
The LLM produces either an answer, a tool request, or another structured action.
03
Inspect output
The runtime validates the model response against schemas, policy and control logic.
04
Act + update
Execute approved tools, persist results and append the outcome to state.
05
Stop or repeat
Return a final answer or continue — subject to explicit budgets and termination rules.
01 — Core mechanism
The loop in pseudocode
The important refinement is architectural: the orchestrator owns the loop. The LLM does not independently keep itself alive; it receives one turn of context at a time.
# simplified production-oriented agent loop
state = initialize_state()
step = 0
while not should_terminate(state, step):
step += 1
messages = build_context(state)
response = call_model(
messages=messages,
tools=tool_schemas
)
if response.type == "final":
persist_final(state, response)
break
elif response.type == "tool_call":
validate_tool_call(response)
result = execute_tool(response)
record_result(state, result)
else:
record_assistant_turn(state, response)
enforce_budgets(state) # time, tokens, cost, stepsBuild the next turnSystem instructions, user goal, conversation history, retrieved facts and prior tool results are packaged into the model context.
Generate a proposed actionThe model may produce a final response or structured tool call. The proposal is not automatically trusted.
Validate before executionCheck tool name, argument schema, permissions, policy, rate limits and dangerous side effects.
Write results back to stateThe model sees the result only on a later turn after the runtime records it.
Terminate deliberatelyA final answer is only one stopping condition. Timeouts, cost limits, repeated failure and explicit cancellation also matter.
02 — Architecture
Six layers that make an agent real
The original infographic is conceptually strong, but a production-grade explanation benefits from separating model intelligence from runtime control.
Conversation + working state
Messages, task plan, retrieved documents, intermediate results, user/session data and structured scratch state maintained outside the model.
Capabilities
Search, APIs, code, databases, files, calculators, email and domain-specific functions. Tools extend reach; they also increase risk.
Stop conditions
Final answer, maximum steps, no-progress detection, timeout, cost ceiling, token budget, explicit user cancel or unrecoverable error.
Control plane
The application decides what context the model receives, what tools are exposed, whether calls are valid and whether another turn should happen.
Context management
Long-running agents must summarize, prune or retrieve selectively. “Just keep appending everything” eventually becomes expensive and brittle.
Logs + traces
Each turn should be inspectable: prompts, tool calls, latency, token usage, errors, costs and stop reason. Otherwise debugging becomes guesswork.
Key correction: “Agent behavior” does not live in the LLM alone.
Behavior emerges from the combination of model capabilities, system instructions, context construction, tool schemas, retrieval, memory policy, orchestration logic, permissions, guardrails and termination strategy. Two products using the same model can behave very differently because their runtimes differ.
03 — Reliability
Production reliability is mostly software engineering
The model can reason, but dependable operation comes from constraints, validation, observability and failure handling around it.
Tool schema validationReject malformed or unauthorized calls before side effects occur.
Idempotency + retriesRetry safely. Avoid duplicate payments, emails, updates or destructive actions.
No-progress detectionDetect repeated reasoning or repeated tool use without meaningful state change.
Budget enforcementCap steps, tokens, latency, tool calls and cost independently of model intent.
Permission boundariesDo not expose every available tool to every task. Use least privilege and user confirmation for high-impact actions.
PRODUCTION CHECKLIST
Minimum controls before deployment
✓ Structured tool schemas and input validation
✓ Explicit max-step and time budgets
✓ Per-tool timeout and retry policy
✓ Trace IDs and iteration logs
✓ User-visible confirmation for consequential actions
✓ Sensitive-data boundaries
✓ Deterministic stop reasons
✓ Fallback behavior for unavailable tools
✓ Evaluation set for common and adversarial tasks
✓ Human escalation path where appropriate
04 — Mental model
Think “runtime + model,” not “magic autonomous mind”
LLM = reasoning engine
Interprets context, proposes next actions and generates language or structured outputs.
Orchestrator = operating logic
Controls the loop, state transitions, tool availability, guardrails and termination.
Tools = external agency
Convert model proposals into effects on the world — which is why validation and permissions matter so much.
05 — FAQ
Common misconceptions
Does the model know it is running in a loop?
Not inherently. It only knows what the application includes in its context. The orchestrator can tell it about prior turns, budgets or loop status, but that is runtime-provided information.
Is every multi-step LLM workflow an “agent”?
No universal definition exists. A useful distinction is that agentic systems choose among possible next actions dynamically, while ordinary workflows follow a predetermined sequence.
Why can agents get stuck?
Typical causes include ambiguous goals, weak tool descriptions, inconsistent state, missing error recovery, context overload, non-idempotent tools or inadequate stopping logic.
What makes an agent safer?
Least-privilege tool access, strong validation, explicit confirmation for high-impact actions, bounded iteration, observability, secure secret handling and policy enforcement outside the model.