Goal-Directed Agents Beyond Prompt Engineering
Goal-directed agents need objectives, planners and feedback loops, not longer prompts. Three loop patterns that move past prompt engineering, with LangGraph.
This article is published in English.
Sources: Agentic Architectures — Article 14: Beyond Prompts — Engineering Goal-Directed Agent Loops
This walkthrough rebuilds the path from raw materials to a working system for: Agentic Architectures — Article 14: Beyond Prompts — Engineering Goal-Directed Agent Loops. The focus is operable steps, explicit checks, and code that you can drop into a repo without guessing intent. For Overview, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
What You’ll Find Here
For What You’ll Find Here, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
The Loop Is the Unit, Not the Prompt
For The Loop Is the Unit, Not the Prompt, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
PROMPT ENGINEERING LOOP ENGINEERING
------------------ ----------------
input --> [model] --> output goal
|
v
+------------------+
| observe state |
+------------------+
|
v
+------------------+
| decide next | <-- this is where
| action | goal-direction lives
+------------------+
|
v
+------------------+
| act |
+------------------+
|
v
+------------------+
| goal met? |--no--> loop back
+------------------+
| yes
v
done
The Three Loop Patterns
For The Three Loop Patterns, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node. For The Three Loop Patterns, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
+------------------+------------------------------------+---------------------------+
| Pattern | Loop Structure | Best For |
+------------------+------------------------------------+---------------------------+
| ReAct | Thought -> Action -> Observation | Exploratory tasks where |
| | repeated until goal met | the path is not known in |
| | Interleaves reasoning and acting | advance |
+------------------+------------------------------------+---------------------------+
| Plan-and-Execute | Plan the full sequence first, | Tasks with a clear |
| | then execute steps | structure that can be |
| | Replan only if execution fails | decomposed upfront |
+------------------+------------------------------------+---------------------------+
| Reflexion | Act, then reflect on the outcome, | Tasks where quality |
| | store the reflection, retry with | improves with iteration |
| | that lesson in context | and self-critique |
+------------------+------------------------------------+---------------------------+
ReAct
For ReAct, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
# harness/loops/react.py
from typing import TypedDict, Annotated, List, Optional
from langgraph.graph import StateGraph, END, START
from langgraph.graph.message import add_messages
from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage, AIMessage
from langchain_aws import ChatBedrock
import boto3
class ReActState(TypedDict):
messages: Annotated[List[BaseMessage], add_messages]
goal: dict # structured goal representation
step_count: int
max_steps: int
goal_met: bool
scratchpad: List[dict] # thought-action-observation history
REACT_SYSTEM_PROMPT = """
You are an agent that solves tasks by interleaving reasoning and action.
For each step:
1. THOUGHT: Reason about the current state and what to do next
2. ACTION: Take one concrete action (call a tool) OR declare the goal met
3. You will then receive an OBSERVATION and repeat
Your goal is provided as a structured object. Check your progress against it
at every step. Do not take actions that do not advance the goal.
When you believe the goal is met, respond with:
GOAL_MET: <brief explanation of how the goal was achieved>
Do not declare the goal met prematurely. Verify against the success criteria.
"""
def build_react_loop(region: str = "us-east-1"):
bedrock = boto3.client("bedrock-runtime", region_name=region)
model = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-5-sonnet-20241022-v2:0",
model_kwargs={"temperature": 0, "max_tokens": 4000},
)
def reason_and_act(state: ReActState) -> ReActState:
goal_desc = format_goal(state["goal"])
scratchpad_text = format_scratchpad(state["scratchpad"])
prompt = f"""
GOAL:
{goal_desc}
PROGRESS SO FAR:
{scratchpad_text or "No steps taken yet."}
What is your next THOUGHT and ACTION? Or is the goal met?
"""
response = model.invoke([
SystemMessage(content=REACT_SYSTEM_PROMPT),
HumanMessage(content=prompt)
])
content = response.content
if "GOAL_MET:" in content:
return {
**state,
"messages": state["messages"] + [response],
"goal_met": True,
}
# Parse thought and action, add to scratchpad
new_scratchpad = list(state["scratchpad"])
new_scratchpad.append({
"step": state["step_count"] + 1,
"thought_action": content,
})
return {
**state,
"messages": state["messages"] + [response],
"step_count": state["step_count"] + 1,
"scratchpad": new_scratchpad,
}
def should_continue(state: ReActState) -> str:
if state.get("goal_met"):
return "done"
if state["step_count"] >= state["max_steps"]:
return "max_steps" # safety limit reached
return "continue"
graph = StateGraph(ReActState)
graph.add_node("reason_act", reason_and_act)
graph.add_node("tools", execute_tools_node) # from Article 5 tool layer
graph.set_entry_point("reason_act")
graph.add_conditional_edges(
"reason_act",
should_continue,
{"continue": "tools", "done": END, "max_steps": END}
)
graph.add_edge("tools", "reason_act")
return graph.compile()
def format_goal(goal: dict) -> str:
lines = [f"Objective: {goal.get('objective', '')}"]
if goal.get("success_criteria"):
lines.append("Success criteria:")
for c in goal["success_criteria"]:
lines.append(f" - {c}")
if goal.get("constraints"):
lines.append("Constraints:")
for c in goal["constraints"]:
lines.append(f" - {c}")
return "\n".join(lines)
def format_scratchpad(scratchpad: List[dict]) -> str:
return "\n\n".join([
f"Step {s['step']}:\n{s['thought_action']}"
for s in scratchpad
])
Plan-and-Execute
For Plan-and-Execute, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
# harness/loops/plan_execute.py
from typing import TypedDict, List, Optional
from langgraph.graph import StateGraph, END, START
from langchain_aws import ChatBedrock
from langchain_core.messages import SystemMessage, HumanMessage
import boto3
import json
class PlanExecuteState(TypedDict):
goal: dict
plan: List[dict] # ordered list of steps
current_step_index: int
completed_steps: List[dict]
needs_replan: bool
goal_met: bool
PLANNER_PROMPT = """
You are a planning agent. Given a goal, produce a complete, ordered plan
of concrete steps that will achieve it.
Each step must be:
- Specific and actionable
- Independently executable
- Clearly tied to the goal
Return JSON:
{
"steps": [
{"step_id": "1", "action": "description", "tool": "tool_name", "expected_result": "..."},
...
]
}
Do not create steps that do not advance the goal. Prefer fewer, larger steps
over many tiny ones. Plan for verification: include a final step that checks
the goal was met.
"""
def build_plan_execute_loop(region: str = "us-east-1"):
bedrock = boto3.client("bedrock-runtime", region_name=region)
# Planner uses the heavy model: planning is where reasoning matters most
planner = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-7-sonnet-20250219-v1:0",
model_kwargs={
"temperature": 0.2,
"max_tokens": 4000,
"thinking": {"type": "enabled", "budget_tokens": 4000},
},
)
# Executor uses the fast model: execution follows the plan
executor = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-5-sonnet-20241022-v2:0",
model_kwargs={"temperature": 0, "max_tokens": 4000},
)
def plan_node(state: PlanExecuteState) -> PlanExecuteState:
goal_desc = format_goal(state["goal"])
response = planner.invoke([
SystemMessage(content=PLANNER_PROMPT),
HumanMessage(content=f"Goal:\n{goal_desc}\n\nProduce a plan.")
])
try:
plan_data = json.loads(response.content)
steps = plan_data.get("steps", [])
except json.JSONDecodeError:
steps = []
return {
**state,
"plan": steps,
"current_step_index": 0,
"needs_replan": False,
}
def execute_node(state: PlanExecuteState) -> PlanExecuteState:
idx = state["current_step_index"]
if idx >= len(state["plan"]):
return {**state, "goal_met": True}
current_step = state["plan"][idx]
response = executor.invoke([
SystemMessage(content="You execute one step of a plan. Focus only on this step."),
HumanMessage(content=f"Execute: {current_step['action']}")
])
# Determine if the step succeeded (simplified)
step_result = {
"step": current_step,
"result": response.content,
"succeeded": "error" not in response.content.lower(),
}
completed = list(state["completed_steps"])
completed.append(step_result)
# If a step failed, flag for replan
if not step_result["succeeded"]:
return {**state, "completed_steps": completed, "needs_replan": True}
return {
**state,
"completed_steps": completed,
"current_step_index": idx + 1,
}
def route_after_execute(state: PlanExecuteState) -> str:
if state.get("needs_replan"):
return "replan"
if state["current_step_index"] >= len(state["plan"]):
return "done"
return "execute"
graph = StateGraph(PlanExecuteState)
graph.add_node("plan", plan_node)
graph.add_node("execute", execute_node)
graph.set_entry_point("plan")
graph.add_edge("plan", "execute")
graph.add_conditional_edges(
"execute",
route_after_execute,
{"execute": "execute", "replan": "plan", "done": END}
)
return graph.compile()
Reflexion
For Reflexion, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts. For Reflexion, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
# harness/loops/reflexion.py
from typing import TypedDict, List, Optional
from langgraph.graph import StateGraph, END
from langchain_aws import ChatBedrock
from langchain_core.messages import SystemMessage, HumanMessage
import boto3
class ReflexionState(TypedDict):
goal: dict
attempt: str # current solution attempt
reflections: List[str] # accumulated lessons from failed attempts
attempt_count: int
max_attempts: int
goal_met: bool
REFLECTION_PROMPT = """
You just attempted to achieve a goal and it did not fully succeed.
Reflect on what went wrong. Be specific and actionable.
Your reflection should identify:
1. What specifically failed or fell short
2. Why it failed (the root cause, not the symptom)
3. What you will do differently next time
This reflection will be available to you on your next attempt.
Make it useful. One or two sentences of genuine insight beats a paragraph
of vague self-criticism.
"""
def build_reflexion_loop(region: str = "us-east-1"):
bedrock = boto3.client("bedrock-runtime", region_name=region)
actor = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-5-sonnet-20241022-v2:0",
model_kwargs={"temperature": 0.3, "max_tokens": 4000},
)
evaluator = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-7-sonnet-20250219-v1:0",
model_kwargs={"temperature": 0, "max_tokens": 2000},
)
def act_node(state: ReflexionState) -> ReflexionState:
goal_desc = format_goal(state["goal"])
reflections_text = ""
if state["reflections"]:
reflections_text = "\n\nLessons from previous attempts:\n" + \
"\n".join(f"- {r}" for r in state["reflections"])
response = actor.invoke([
SystemMessage(content="You solve tasks. Apply lessons from past attempts."),
HumanMessage(content=f"Goal:\n{goal_desc}{reflections_text}\n\nProduce your solution.")
])
return {
**state,
"attempt": response.content,
"attempt_count": state["attempt_count"] + 1,
}
def evaluate_node(state: ReflexionState) -> ReflexionState:
goal_desc = format_goal(state["goal"])
response = evaluator.invoke([
SystemMessage(content="Evaluate whether the solution meets the goal. Respond GOAL_MET or GOAL_NOT_MET with a brief reason."),
HumanMessage(content=f"Goal:\n{goal_desc}\n\nSolution:\n{state['attempt']}")
])
if "GOAL_MET" in response.content and "NOT_MET" not in response.content:
return {**state, "goal_met": True}
# Generate a reflection for the next attempt
reflection_response = actor.invoke([
SystemMessage(content=REFLECTION_PROMPT),
HumanMessage(content=f"Goal:\n{goal_desc}\n\nYour attempt:\n{state['attempt']}\n\nEvaluation:\n{response.content}")
])
new_reflections = list(state["reflections"])
new_reflections.append(reflection_response.content)
return {**state, "reflections": new_reflections, "goal_met": False}
def should_retry(state: ReflexionState) -> str:
if state.get("goal_met"):
return "done"
if state["attempt_count"] >= state["max_attempts"]:
return "exhausted"
return "retry"
graph = StateGraph(ReflexionState)
graph.add_node("act", act_node)
graph.add_node("evaluate", evaluate_node)
graph.set_entry_point("act")
graph.add_edge("act", "evaluate")
graph.add_conditional_edges(
"evaluate",
should_retry,
{"retry": "act", "done": END, "exhausted": END}
)
return graph.compile()
Representing the Goal
For Representing the Goal, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
# harness/loops/goal.py
from dataclasses import dataclass, field
from typing import List, Optional, Callable
from enum import Enum
import time
class GoalStatus(Enum):
ACTIVE = "active"
ACHIEVED = "achieved"
FAILED = "failed"
REVISED = "revised"
ABANDONED = "abandoned"
@dataclass
class Goal:
"""
A structured goal that an agent loop pursues.
Distinct from the initial prompt: this is what the agent
checks progress against at every step.
"""
goal_id: str
objective: str # what to achieve
success_criteria: List[str] # how to know it is done
constraints: List[str] # boundaries that must hold
priority: int = 1
parent_goal_id: Optional[str] = None # for sub-goals
status: GoalStatus = GoalStatus.ACTIVE
created_at: float = field(default_factory=time.time)
progress_notes: List[str] = field(default_factory=list)
def to_prompt_block(self) -> str:
"""Renders the goal for injection into agent context."""
lines = [f"OBJECTIVE: {self.objective}", ""]
lines.append("SUCCESS CRITERIA (all must be met):")
for i, c in enumerate(self.success_criteria, 1):
lines.append(f" {i}. {c}")
if self.constraints:
lines.append("\nCONSTRAINTS (must not be violated):")
for c in self.constraints:
lines.append(f" - {c}")
if self.progress_notes:
lines.append("\nPROGRESS SO FAR:")
for note in self.progress_notes[-3:]: # last 3 notes
lines.append(f" - {note}")
return "\n".join(lines)
def record_progress(self, note: str):
self.progress_notes.append(note)
class GoalTracker:
"""
Manages the goal hierarchy for an agent run.
Supports sub-goals, revision, and progress assessment.
"""
def __init__(self):
self.goals: dict = {}
self.root_goal_id: Optional[str] = None
def set_root_goal(self, goal: Goal):
self.goals[goal.goal_id] = goal
self.root_goal_id = goal.goal_id
def add_subgoal(self, parent_id: str, subgoal: Goal):
subgoal.parent_goal_id = parent_id
self.goals[subgoal.goal_id] = subgoal
def assess_progress(
self,
goal_id: str,
current_state: str,
assessor_model,
) -> dict:
"""
Uses a model to assess how much progress has been made
toward a goal's success criteria.
"""
goal = self.goals.get(goal_id)
if not goal:
return {"error": "goal not found"}
from langchain_core.messages import SystemMessage, HumanMessage
import json
response = assessor_model.invoke([
SystemMessage(content="""
Assess progress toward a goal. For each success criterion, determine
whether it is met, partially met, or not met.
Return JSON:
{
"criteria_status": [{"criterion": "...", "status": "met|partial|not_met"}],
"overall_progress": 0.0 to 1.0,
"goal_achieved": true | false,
"blocking_issues": ["..."]
}
"""),
HumanMessage(content=f"Goal:\n{goal.to_prompt_block()}\n\nCurrent state:\n{current_state}")
])
try:
return json.loads(response.content)
except json.JSONDecodeError:
return {"overall_progress": 0.0, "goal_achieved": False}
Runtime Goal Injection and Revision
For Runtime Goal Injection and Revision, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
# harness/loops/goal_revision.py
import boto3
import time
import json
from typing import Optional
from harness.loops.goal import Goal, GoalStatus, GoalTracker
class RuntimeGoalManager:
"""
Handles goal injection and revision during a running loop.
Supports the /goal directive: a user or supervisor can modify
the agent's goal mid-run without restarting.
"""
def __init__(
self,
tracker: GoalTracker,
region: str = "us-east-1",
):
self.tracker = tracker
bedrock = boto3.client("bedrock-runtime", region_name=region)
from langchain_aws import ChatBedrock
self.reconciler = ChatBedrock(
client=bedrock,
model_id="anthropic.claude-3-7-sonnet-20250219-v1:0",
model_kwargs={"temperature": 0.2, "max_tokens": 3000},
)
def inject_goal_revision(
self,
current_goal_id: str,
revision_type: str, # "add_constraint" | "modify_objective" | "redirect"
revision_content: str,
completed_work: str,
) -> dict:
"""
Injects a goal revision into a running loop.
Returns a reconciliation that tells the agent what work is still valid.
"""
current_goal = self.tracker.goals.get(current_goal_id)
if not current_goal:
return {"error": "goal not found"}
from langchain_core.messages import SystemMessage, HumanMessage
response = self.reconciler.invoke([
SystemMessage(content="""
The agent's goal has been revised mid-run. Your job is to reconcile:
1. Determine what completed work is still valid under the new goal
2. Determine what completed work must be discarded or redone
3. Determine what new work the revision requires
Do NOT restart from scratch unless the revision fundamentally changes
the objective. Preserve as much valid work as possible.
Return JSON:
{
"work_still_valid": ["..."],
"work_to_discard": ["..."],
"new_work_required": ["..."],
"requires_full_restart": true | false,
"reconciliation_note": "brief explanation for the agent"
}
"""),
HumanMessage(content=f"""
Original goal:
{current_goal.to_prompt_block()}
Revision type: {revision_type}
Revision: {revision_content}
Work completed so far:
{completed_work}
""")
])
try:
reconciliation = json.loads(response.content)
except json.JSONDecodeError:
reconciliation = {"requires_full_restart": False, "reconciliation_note": revision_content}
# Apply the revision to the goal object
if revision_type == "add_constraint":
current_goal.constraints.append(revision_content)
elif revision_type == "modify_objective":
current_goal.objective = revision_content
current_goal.status = GoalStatus.REVISED
elif revision_type == "redirect":
current_goal.objective = revision_content
current_goal.success_criteria = [] # will be re-derived
current_goal.status = GoalStatus.REVISED
current_goal.record_progress(
f"Goal revised ({revision_type}): {reconciliation.get('reconciliation_note', '')}"
)
return reconciliation
# Example of the /goal directive as a user-facing command
def handle_goal_directive(
directive: str,
goal_manager: RuntimeGoalManager,
current_goal_id: str,
completed_work: str,
) -> dict:
"""
Parses a /goal directive from a user and applies it to the running loop.
Directive formats:
/goal add-constraint <text> - add a constraint to the current goal
/goal redirect <text> - change the objective
/goal status - report current goal and progress
"""
parts = directive.strip().split(maxsplit=2)
if len(parts) < 2 or parts[0] != "/goal":
return {"error": "invalid directive format"}
command = parts[1]
if command == "status":
goal = goal_manager.tracker.goals.get(current_goal_id)
return {"goal_status": goal.to_prompt_block() if goal else "no active goal"}
if command == "add-constraint" and len(parts) == 3:
return goal_manager.inject_goal_revision(
current_goal_id, "add_constraint", parts[2], completed_work
)
if command == "redirect" and len(parts) == 3:
return goal_manager.inject_goal_revision(
current_goal_id, "redirect", parts[2], completed_work
)
return {"error": f"unknown goal command: {command}"}
Choosing and Combining Loop Patterns
For Choosing and Combining Loop Patterns, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness. For Choosing and Combining Loop Patterns, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
+---------------------------------+--------------------------------------+
| Situation | Loop Choice |
+---------------------------------+--------------------------------------+
| Path to goal is unknown | ReAct: figure out each step as you go|
+---------------------------------+--------------------------------------+
| Task has clear structure | Plan-and-Execute: plan then run |
+---------------------------------+--------------------------------------+
| Quality improves with iteration | Reflexion: act, reflect, retry |
+---------------------------------+--------------------------------------+
| Complex task, mixed sub-tasks | Plan-and-Execute at top, |
| | ReAct or Reflexion per step |
+---------------------------------+--------------------------------------+
| High stakes, needs verification | Any loop + Article 5 verification |
| | + Article 13 approval gate |
+---------------------------------+--------------------------------------+
# harness/loops/loop_selector.py
from enum import Enum
from typing import Optional
class LoopPattern(Enum):
REACT = "react"
PLAN_EXECUTE = "plan_execute"
REFLEXION = "reflexion"
def select_loop_pattern(
task_complexity: str, # from Article 10 complexity classifier
has_procedure: bool, # from Article 7 procedural memory
is_exploratory: bool,
quality_critical: bool,
) -> LoopPattern:
"""
Selects the loop pattern based on task characteristics.
Connects loop engineering to the memory and cost systems built earlier.
"""
# If we have a known-good procedure, plan-and-execute with it
if has_procedure:
return LoopPattern.PLAN_EXECUTE
# Exploratory tasks with unknown paths suit ReAct
if is_exploratory:
return LoopPattern.REACT
# Quality-critical tasks benefit from iterative refinement
if quality_critical:
return LoopPattern.REFLEXION
# Structured, complex tasks: plan first
if task_complexity in ("COMPLEX", "CRITICAL"):
return LoopPattern.PLAN_EXECUTE
# Default: ReAct is the most flexible
return LoopPattern.REACT
Production Reality Check
For Production Reality Check, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
Reference Architecture
For Reference Architecture, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
Task arrives with a goal
|
v
+---------------------------+
| Goal Structuring |
| objective + criteria + |
| constraints (Goal obj) |
+---------------------------+
|
v
+---------------------------+
| Loop Pattern Selector |
| has_procedure? -> P&E |
| exploratory? -> ReAct |
| quality-crit? -> Reflex|
+---------------------------+
|
+-----+-----+-----------+
| | |
v v v
+--------+ +--------+ +----------+
| ReAct | | Plan & | |Reflexion |
| loop | |Execute | | loop |
+--------+ +--------+ +----------+
| | |
+-----+-----+-----------+
|
v
+---------------------------+
| Each step runs through: |
| - Harness (Article 5) |
| - Tools (auth/retry/cb) |
| - Memory (Article 7) |
+---------------------------+
|
v
+---------------------------+
| Goal Progress Assessment|
| goal met? revise? stop? |
+---------------------------+
|
+------+------+
| |
v v
Runtime Goal met --> Verification (Art 5)
goal + eval (Art 8)
revision + approval if needed (Art 13)
(/goal)
Reference Infrastructure Stack
For Reference Infrastructure Stack, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
+-----------------------------+---------------------+------------------------------+
| Component | Technology | Role |
+-----------------------------+---------------------+------------------------------+
| ReAct Loop | LangGraph + | Interleaved reason-act for |
| | Claude 3.5 | exploratory tasks |
+-----------------------------+---------------------+------------------------------+
| Plan-and-Execute Loop | LangGraph + | Plan with heavy model, |
| | Claude 3.7 / 3.5 | execute with fast model |
+-----------------------------+---------------------+------------------------------+
| Reflexion Loop | LangGraph + | Act-reflect-retry for |
| | Claude 3.5 / 3.7 | iterative quality |
+-----------------------------+---------------------+------------------------------+
| Goal Representation | Structured Goal | Objective, criteria, |
| | dataclass | constraints, progress |
+-----------------------------+---------------------+------------------------------+
| Goal Tracker | In-memory + state | Sub-goal hierarchy, |
| | | progress assessment |
+-----------------------------+---------------------+------------------------------+
| Runtime Goal Revision | Custom + Claude 3.7 | /goal directive, mid-run |
| | reconciler | goal injection and merge |
+-----------------------------+---------------------+------------------------------+
| Loop Pattern Selector | Custom classifier | Chooses loop per task, |
| | | connects to Article 7 memory |
+-----------------------------+---------------------+------------------------------+
| Step-Level Harness | Article 5 | Shapes model behavior |
| | | within each loop step |
+-----------------------------+---------------------+------------------------------+
| Goal Revision Security | Article 9 controls | Treats external goal changes |
| | | as data, gates high-privilege|
+-----------------------------+---------------------+------------------------------+
| Loop Safety Limits | max_steps / | Prevents infinite loops, |
| | max_attempts | ties to Article 5 loop detect|
+-----------------------------+---------------------+------------------------------+
| Local Dev Alternative | Ollama + LangGraph | All three loops testable |
| | Docker Compose | without Bedrock costs |
+-----------------------------+---------------------+------------------------------+
For Reference Infrastructure Stack, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
Operational checklist
For Operational checklist, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state.
Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments.
Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
Add a smoke test that exercises the critical path in CI with fixtures, not live paid APIs, whenever budgets allow.
Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
Before promoting the stack, freeze versions, capture a golden transcript for the critical path, and confirm rollback steps. Shared environments need rate limits, tenancy checks, and a clear owner for secret rotation. Prefer boring reliability over clever one-off demos.