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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 23:52:15 +09:00

4.3 KiB

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id title category status canonical_id aliases duplicate_of source_trust_level confidence_score verification_status tags raw_sources last_reinforced github_commit tech_stack
wiki-2026-0508-e-component-execution-loop E-component (Execution Loop) 10_Wiki/Topics verified self
Execution Loop
E-Loop
Agent Runtime Loop
none A 0.9 applied
agent
runtime
llm
architecture
2026-05-10 pending
language framework
Python anthropic-sdk

E-component (Execution Loop)

매 한 줄

"매 LLM agent 의 heartbeat". 매 EST (Execution / State / Tools) component triad 의 E — 매 model-call → 매 tool-dispatch → 매 result-feedback 의 inner loop. 매 Claude Agent SDK / OpenAI Assistants / LangGraph 매 same primitive.

매 핵심

매 mechanism

  1. Send messages + tool definitions to LLM.
  2. LLM 매 returns text + (optional) tool_use blocks.
  3. If tool_use: dispatch to T-component (Tool Registry), append tool_result to state.
  4. Loop until 매 stop_reason == "end_turn" or max_iterations.

매 components

  • E (this): orchestrator — message-pump.
  • S (State Store): conversation history, scratch state.
  • T (Tool Registry): handler dispatch, schema validation.

매 응용

  1. Code agents (Claude Code, Cursor, Devin).
  2. Research agents (Perplexity, Deep Research).
  3. Workflow automation.

💻 패턴

Minimal execution loop (Anthropic SDK 2026)

from anthropic import Anthropic
client = Anthropic()

def run(messages, tools, dispatch, max_iters=20):
    for _ in range(max_iters):
        resp = client.messages.create(
            model="claude-opus-4-7",
            max_tokens=4096,
            tools=tools,
            messages=messages,
        )
        messages.append({"role": "assistant", "content": resp.content})
        if resp.stop_reason == "end_turn":
            return resp
        tool_results = [
            {"type": "tool_result", "tool_use_id": b.id, "content": dispatch(b.name, b.input)}
            for b in resp.content if b.type == "tool_use"
        ]
        messages.append({"role": "user", "content": tool_results})
    raise RuntimeError("max iterations exceeded")

Streaming variant

with client.messages.stream(model="claude-opus-4-7", messages=messages, tools=tools) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            print(event.delta.text, end="", flush=True)
    final = stream.get_final_message()

Tool dispatch (T-component plug-in)

TOOLS = {
    "read_file": lambda input: open(input["path"]).read(),
    "list_dir":  lambda input: os.listdir(input["path"]),
}
def dispatch(name, input):
    try: return TOOLS[name](input)
    except Exception as e: return f"Error: {e}"

Stop conditions

STOP = {"end_turn", "stop_sequence", "max_tokens"}
if resp.stop_reason in STOP: break

Prompt caching the system + tools

resp = client.messages.create(
    model="claude-opus-4-7",
    system=[{"type": "text", "text": SYSTEM, "cache_control": {"type": "ephemeral"}}],
    tools=[{**t, "cache_control": {"type": "ephemeral"}} for t in tools],
    messages=messages,
)

Budget guard

total_tokens = 0
while True:
    resp = client.messages.create(...)
    total_tokens += resp.usage.input_tokens + resp.usage.output_tokens
    if total_tokens > BUDGET: raise BudgetExceeded()

매 결정 기준

상황 Approach
Single-turn task Direct API call
Multi-tool task E-loop
Long-running workflow E-loop + checkpointing (S)

기본값: E-loop + prompt caching + budget guard + max-iter clamp.

🔗 Graph

🤖 LLM 활용

언제: building agent runtime, multi-step tool-use task. 언제 X: pure single-shot prompt (no tools, no state).

안티패턴

  • No max-iter cap: 매 infinite-loop 의 risk.
  • No budget guard: 매 unbounded cost.
  • Recreating system prompt per turn: cache miss → 매 5-10x cost.

🧪 검증 / 중복

  • Verified (Anthropic Messages API docs, Claude Agent SDK).
  • 신뢰도 A.

🕓 Changelog

날짜 변경
2026-05-08 Phase 1
2026-05-10 Manual cleanup — E-component FULL with SDK 2026 loop patterns