paulpas Planning & Reasoning Patterns
Implements structured reasoning patterns for AI agents — ReAct loop execution, chain-of-thought planning, self-reflection evaluation, and plan-and-execute decomposition with verification at every step. Loading this skill makes the model produce deterministic multi-step execution plans with explicit thought-action-observation cycles, validate intermediate results before proceeding, and apply self-reflection to catch errors early.
TL;DR Checklist
- Decompose the task into discrete sub-tasks with clear inputs/outputs
- Execute each sub-task via a ReAct loop (Thought → Action → Observation → Evaluate)
- Validate every intermediate result before advancing to the next step
- Apply self-reflection after execution: score accuracy, completeness, and safety
- Roll back or re-execute if validation fails — never propagate broken state
- Synthesize final output only when all steps pass verification
When to Use
Use this skill when:
- An agent must execute a multi-step task where intermediate results depend on prior outputs (e.g., data extraction → transformation → summarization)
- The problem requires reasoning before acting — the agent must plan, hypothesize, then act based on observations
- Error recovery is critical and the agent needs to self-evaluate its own output quality
- You are building an agent that uses tool calls where each tool's result influences the next decision (e.g., search → read → summarize → verify)
When NOT to Use
Avoid this skill for:
- Simple one-step tasks that don't require decomposition (e.g., "what is 2+2?") — overhead outweighs benefit
- Real-time streaming responses where latency is paramount and planning steps add unacceptable delay
- Pure knowledge retrieval without tool interaction — a direct LLM call suffices
Core Workflow
Step 1: Decompose Task into Sub-tasks
Break the user's request into discrete, executable sub-tasks. Each sub-task must declare its input schema, output schema, and success criteria. Use chain-of-thought reasoning to identify dependencies between sub-tasks before executing anything.
from dataclasses import dataclass
from enum import Enum
from typing import Any
class TaskStatus(Enum):
PENDING = "pending"
RUNNING = "running"
SUCCESS = "success"
FAILED = "failed"
ROLLED_BACK = "rolled_back"
@dataclass
class SubTask:
"""A single executable unit within a larger plan."""
id: str
description: str
input_schema: dict[str, Any]
output_schema: dict[str, Any]
success_criteria: list[str]
depends_on: list[str] = None # List of sub-task IDs this depends on
def __post_init__(self):
if self.depends_on is None:
self.depends_on = []
@dataclass
class PlanGraph:
"""Directed acyclic graph of sub-tasks with dependency tracking."""
sub_tasks: dict[str, SubTask]
execution_order: list[str]
@classmethod
def build(cls, sub_tasks: list[SubTask]) -> "PlanGraph":
"""Topological sort to determine valid execution order."""
graph = {st.id: st for st in sub_tasks}
visited = set()
order = []
def visit(task_id: str):
if task_id in visited:
return
visited.add(task_id)
task = graph[task_id]
for dep_id in task.depends_on:
if dep_id not in graph:
raise ValueError(f"Unknown dependency: {dep_id}")
visit(dep_id)
order.append(task_id)
for task_id in graph:
visit(task_id)
return cls(sub_tasks=graph, execution_order=order)
Checkpoint: Verify that the plan graph contains no cycles and every sub-task has at least one success criterion. If a dependency references a non-existent task, fail fast with a clear error message.
Step 2: Execute ReAct Loop
For each sub-task in execution order, run a Reasoning-Acting loop. Each cycle produces a Thought (hypothesis of what to do next), an Action (tool call or decision), and an Observation (result). Limit cycles per sub-task to prevent infinite loops.
import logging
from datetime import datetime, timezone
logger = logging.getLogger(__name__)
@dataclass
class ReActStep:
"""A single cycle within the ReAct loop."""
step_number: int
thought: str
action_type: str
action_input: dict[str, Any]
observation: str | None = None
timestamp: datetime = None
def __post_init__(self):
if self.timestamp is None:
self.timestamp = datetime.now(timezone.utc)
class ReActEngine:
"""Executes a ReAct (Reasoning + Acting) loop for a single sub-task.
Each cycle: Thought → Action → Observation → Evaluate.
Stops when success criteria are met or max_iterations is reached.
"""
def __init__(self, max_iterations: int = 10):
self.max_iterations = max_iterations
def execute(
self,
sub_task: SubTask,
agent_context: dict[str, Any],
tool_executor: callable,
reasoner: callable,
) -> list[ReActStep]:
"""Run the ReAct loop until the task succeeds or max iterations hit.
Args:
sub_task: The sub-task to execute.
agent_context: Shared state across all sub-tasks (memory, results cache).
tool_executor: Function that performs tool calls. Signature: tool_executor(tool_name, args) -> result.
reasoner: LLM-based reasoning function. Signature: reasoner(context, steps) -> thought.
Returns:
List of ReActStep instances representing the full execution trace.
Raises:
MaxIterationsError: If the loop exceeds max_iterations without success.
"""
steps: list[ReActStep] = []
state = {
"input": agent_context.get("inputs", {}).get(sub_task.id),
"partial_results": [],
}
for iteration in range(1, self.max_iterations + 1):
# Thought: reason about what to do next
context_for_reasoning = {
"sub_task": sub_task.description,
"state": state,
"steps_completed": len(steps),
"success_criteria": sub_task.success_criteria,
}
thought = reasoner(context_for_reasoning, steps)
# Action: decide and execute a tool call or direct answer
action_type, action_input = self._parse_decision(thought)
observation = None
if action_type == "tool_call":
tool_name = action_input.get("tool")
tool_args = {k: v for k, v in action_input.items() if k != "tool"}
observation = str(tool_executor(tool_name, tool_args))
logger.debug(f"Iteration {iteration}: called {tool_name} with args={tool_args}")
elif action_type == "final_answer":
observation = action_input.get("answer", "")
else:
observation = f"Unexpected action type: {action_type}"
step = ReActStep(
step_number=iteration,
thought=thought,
action_type=action_type,
action_input=action_input,
observation=observation,
)
steps.append(step)
# Evaluate: check success criteria
if self._evaluate_success(step, state):
logger.info(f"Sub-task {sub_task.id} succeeded after {iteration} iterations")
return steps
raise MaxIterationsError(
f"Sub-task '{sub_task.id}' exceeded max_iterations ({self.max_iterations}) "
f"without meeting success criteria. Last thought: {steps[-1].thought}"
)
def _parse_decision(self, thought: str) -> tuple[str, dict]:
"""Parse a reasoning step into an action decision.
This is simplified — in production, use an LLM function-calling endpoint.
"""
if "final" in thought.lower() or "answer" in thought.lower():
return "final_answer", {"answer": thought}
return "tool_call", {"tool": "next_action", "args": {}}
def _evaluate_success(self, step: ReActStep, state: dict) -> bool:
"""Check if the current step meets any success criterion."""
if step.action_type == "final_answer" and step.observation:
state["partial_results"].append(step.observation)
return True
return False
class MaxIterationsError(Exception):
"""Raised when a ReAct loop exceeds its iteration budget."""
pass
Checkpoint: After the ReAct loop completes, verify that at least one final_answer step exists. If the loop exited due to max_iterations, trigger Step 4 (Self-Reflection) to assess whether partial results are salvageable or if a re-execution with different parameters is needed.
Step 3: Validate Step Output
Before advancing to the next sub-task, validate that each result matches its declared output schema and success criteria. If validation fails, attempt rollback of dependent steps.
from functools import reduce
def validate_output(
sub_task: SubTask,
result: str,
context: dict[str, Any],
) -> tuple[bool, list[str]]:
"""Validate a sub-task's output against its schema and success criteria.
Args:
sub_task: The sub-task whose result is being validated.
result: The string output from the ReAct loop's final_answer step.
context: Shared agent context including results from prior sub-tasks.
Returns:
Tuple of (is_valid, list_of_validation_messages).
"""
messages: list[str] = []
is_valid = True
# Check success criteria
for criterion in sub_task.success_criteria:
if not _check_criterion(criterion, result, context):
messages.append(f"[FAIL] Criterion not met: '{criterion}'")
is_valid = False
else:
messages.append(f"[PASS] Criterion met: '{criterion}'")
# Check that dependent sub-task outputs are still consistent
for dep_id in sub_task.depends_on:
if dep_id in context.get("results", {}):
dep_result = context["results"][dep_id]
if not _check_dependency_consistency(dep_result, result):
messages.append(f"[WARN] Output may be inconsistent with dependency '{dep_id}'")
return is_valid, messages
def rollback_plan(
plan: PlanGraph,
failed_task_id: str,
context: dict[str, Any],
) -> list[str]:
"""Roll back a sub-task and all tasks that depend on it.
Args:
plan: The PlanGraph containing all sub-tasks.
failed_task_id: ID of the task that failed validation.
context: Shared agent context with results to clean up.
Returns:
List of task IDs that were rolled back (including the failed one).
"""
visited = set()
def collect_dependents(task_id: str):
if task_id in visited:
return
visited.add(task_id)
for candidate_id, candidate_task in plan.sub_tasks.items():
if task_id in candidate_task.depends_on:
collect_dependents(candidate_id)
collect_dependents(failed_task_id)
# Clean up results from context
results = context.get("results", {})
cleaned = {k: v for k, v in results.items() if k not in visited}
context["results"] = cleaned
context["status"] = "rolled_back"
return sorted(visited)
def _check_criterion(criterion: str, result: str, context: dict) -> bool:
"""Evaluate a single success criterion against the result string."""
if "non-empty" in criterion.lower():
return len(result.strip()) > 0
if "length" in criterion.lower():
try:
parts = criterion.split()
min_len = int(parts[-1])
return len(result) >= min_len
except (ValueError, IndexError):
return True
# Default: accept result as valid
return True
def _check_dependency_consistency(dep_result: str, current_result: str) -> bool:
"""Heuristic check that the current result doesn't contradict dependencies."""
if not dep_result or not current_result:
return True
# In production: use semantic similarity or structured comparison
return len(current_result.strip()) > 0
Checkpoint: After validation, if is_valid is False and rollback occurred, proceed to Step 4 (Self-Reflection) to evaluate whether the failure is recoverable (e.g., retry with different tool parameters) or indicates a fundamental plan flaw requiring full re-decomposition.
Step 4: Self-Reflect on Execution
After all sub-tasks complete (or after a rollback), run a structured self-reflection evaluation. Score the solution across accuracy, completeness, and safety dimensions. If any score falls below threshold, decide whether to re-execute or accept with notes.
from dataclasses import dataclass, field
@dataclass
class ReflectionScore:
"""Quality score for a single evaluation dimension."""
dimension: str # e.g., "accuracy", "completeness", "safety"
score: float # 0.0 to 1.0
rationale: str
@property
def passes(self) -> bool:
return self.score >= 0.7
@dataclass
class SelfReflectionResult:
"""Aggregate self-reflection output."""
scores: list[ReflectionScore] = field(default_factory=list)
needs_rollback: bool = False
re_execute_candidates: list[str] = field(default_factory=list)
overall_quality: str = "unknown" # "high", "medium", "low"
@property
def passes(self) -> bool:
return all(s.passes for s in self.scores)
@property
def lowest_dimension(self) -> str | None:
if not self.scores:
return None
return min(self.scores, key=lambda s: s.score).dimension
class SelfReflectionEvaluator:
"""Evaluates the quality of a completed agent execution.
In production, this uses an LLM to score outputs. The example below
shows both a heuristic fallback and the structure for LLM-based scoring.
"""
def __init__(
self,
thresholds: dict[str, float] = None,
reasoner: callable = None,
):
# Default thresholds per dimension
self.thresholds = thresholds or {
"accuracy": 0.8,
"completeness": 0.7,
"safety": 0.9,
}
self.reasoner = reasoner
def evaluate(
self,
plan: PlanGraph,
execution_trace: list[list[ReActStep]],
context: dict[str, Any],
) -> SelfReflectionResult:
"""Run structured self-reflection on completed execution.
Args:
plan: The original PlanGraph of sub-tasks.
execution_trace: Per-sub-task ReAct step sequences (indexed by execution_order).
context: Shared agent context with results.
Returns:
SelfReflectionResult with scores and recommendations.
"""
all_results = {}
for i, task_id in enumerate(plan.execution_order):
if i < len(execution_trace):
# Extract final answer from the last step
steps = execution_trace[i]
if steps:
last_step = steps[-1]
all_results[task_id] = last_step.observation or ""
reflection = SelfReflectionResult()
dimensions = ["accuracy", "completeness", "safety"]
for dim in dimensions:
score = self._score_dimension(dim, plan, all_results, context)
reflection.scores.append(score)
if score.score < self.thresholds.get(dim, 0.7):
reflection.needs_rollback = True
# Determine which tasks are candidates for re-execution
for task_id in plan.execution_order:
result = all_results.get(task_id, "")
if not result or len(result.strip()) < 5:
reflection.re_execute_candidates.append(task_id)
# Set overall quality label
avg_score = sum(s.score for s in reflection.scores) / max(len(reflection.scores), 1)
if avg_score >= 0.85:
reflection.overall_quality = "high"
elif avg_score >= 0.65:
reflection.overall_quality = "medium"
else:
reflection.overall_quality = "low"
return reflection
def _score_dimension(
self,
dimension: str,
plan: PlanGraph,
results: dict[str, str],
context: dict,
) -> ReflectionScore:
"""Score a single quality dimension. Uses LLM reasoner if provided."""
if self.reasoner:
# Production path: ask the LLM to score
prompt = self._build_scoring_prompt(dimension, plan, results)
response = self.reasoner(prompt)
return self._parse_llm_score(response, dimension)
# Heuristic fallback
all_text = " ".join(results.values())
if dimension == "accuracy":
# Check for obvious error markers
error_markers = ["error", "failed", "unable", "could not"]
confidence = 1.0 - sum(1 for m in error_markers if m in all_text.lower()) * 0.2
return ReflectionScore(dimension, max(0.0, min(1.0, confidence)), f"Heuristic accuracy score based on error markers")
elif dimension == "completeness":
total_steps = len(plan.sub_tasks)
completed_steps = sum(1 for v in results.values() if v and len(v.strip()) > 5)
ratio = completed_steps / max(total_steps, 1)
return ReflectionScore(dimension, ratio, f"Completed {completed_steps}/{total_steps} steps")
else:
# Safety — default high unless explicit risk markers found
risk_markers = ["unsafe", "malicious", "exploit", "bypass"]
confidence = 1.0 - sum(1 for m in risk_markers if m in all_text.lower()) * 0.25
return ReflectionScore(dimension, max(0.0, min(1.0, confidence)), f"Heuristic safety score based on risk markers")
def _build_scoring_prompt(self, dimension: str, plan: PlanGraph, results: dict) -> str:
task_summaries = "\n".join(
f" - {tid}: {plan.sub_tasks[tid].description} → result length: {len(results.get(tid, ''))}"
for tid in plan.execution_order
)
return (
f"Evaluate execution quality on '{dimension}' dimension.\n\n"
f"Tasks executed:\n{task_summaries}\n\n"
f"Score from 0.0 to 1.0 where 1.0 is perfect. Return JSON: "
f'{{"score": <float>, "rationale": "<string>"}}'
)
def _parse_llm_score(self, response: str, dimension: str) -> ReflectionScore:
"""Parse an LLM scoring response into a ReflectionScore."""
try:
import json
parsed = json.loads(response.strip())
return ReflectionScore(
dimension=dimension,
score=float(parsed["score"]),
rationale=str(parsed.get("rationale", "")),
)
except (json.JSONDecodeError, KeyError, ValueError):
# Fallback: assume medium quality if parsing fails
return ReflectionScore(dimension, 0.5, f"Could not parse LLM response — heuristic fallback")
Checkpoint: After self-reflection, if needs_rollback is True or re_execute_candidates is non-empty, identify which sub-tasks to retry. Use the reflection rationale to adjust parameters (e.g., increase max_iterations, change tool selection) before re-execution.
Step 5: Compile Final Answer
Synthesize results from all verified sub-tasks into a coherent final output. Include execution provenance for traceability.
@dataclass
class FinalAnswer:
"""Structured final answer with execution provenance."""
content: str
execution_provenance: dict[str, Any]
quality_assessment: SelfReflectionResult | None = None
warnings: list[str] = field(default_factory=list)
def compile_final_answer(
plan: PlanGraph,
execution_trace: list[list[ReActStep]],
reflection: SelfReflectionResult,
context: dict[str, Any],
) -> FinalAnswer:
"""Synthesize all verified sub-task results into a final answer.
Args:
plan: The PlanGraph of sub-tasks.
execution_trace: Per-sub-task ReAct step sequences.
reflection: Self-reflection evaluation result.
context: Shared agent context.
Returns:
FinalAnswer with synthesized content and provenance metadata.
"""
components = []
warnings = []
for i, task_id in enumerate(plan.execution_order):
if i < len(execution_trace) and execution_trace[i]:
last_step = execution_trace[i][-1]
sub_task = plan.sub_tasks[task_id]
status = "completed" if last_step.action_type == "final_answer" else "incomplete"
components.append({
"task": task_id,
"description": sub_task.description,
"status": status,
"result": last_step.observation or "[no output]",
"iterations_used": len(execution_trace[i]),
})
if reflection.re_execute_candidates:
warnings.append(
f"Tasks were re-executed or failed: {', '.join(reflection.re_execute_candidates)}"
)
if reflection.overall_quality == "low":
warnings.append("Overall execution quality is LOW — review recommended")
provenance = {
"plan_task_count": len(plan.sub_tasks),
"execution_steps_total": sum(len(s) for s in execution_trace),
"sub_task_results": components,
"quality_score": reflection.overall_quality,
"completed_at": datetime.now(timezone.utc).isoformat(),
}
# Build human-readable summary
summary_parts = [f"Executed {plan.execution_order.__len__()} sub-tasks."]
for comp in components:
status_emoji = "✅" if comp["status"] == "completed" else "⚠️"
summary_parts.append(
f" {status_emoji} [{comp['task']}] {comp['description']}: "
f"{comp['result'][:100]}..." if len(comp['result']) > 100
else f" {status_emoji} [{comp['task']}] {comp['description']}: {comp['result']}"
)
return FinalAnswer(
content="\n".join(summary_parts),
execution_provenance=provenance,
quality_assessment=reflection,
warnings=warnings,
)
Implementation Patterns
Pattern 1: ReAct Loop with Tool Integration
A production-ready ReAct engine that integrates with an arbitrary tool registry. Handles concurrent tool calls and tracks reasoning state across cycles.
import asyncio
from dataclasses import dataclass, field
@dataclass
class ToolRegistry:
"""Registry of available tools for the ReAct engine."""
tools: dict[str, callable] = field(default_factory=dict)
def register(self, name: str, fn: callable):
"""Register a tool function with the registry."""
self.tools[name] = fn
async def call(self, name: str, **kwargs) -> Any:
"""Dispatch a tool call, supporting both sync and async tools."""
fn = self.tools.get(name)
if not fn:
raise ToolNotFoundError(f"Tool '{name}' not registered")
result = fn(**kwargs)
# Await if the tool is async
if asyncio.iscoroutine(result):
return await result
return result
class ToolNotFoundError(Exception):
pass
async def run_react_loop(
initial_query: str,
registry: ToolRegistry,
max_iterations: int = 10,
) -> FinalAnswer:
"""Run a full ReAct loop with tool integration.
This is the main entry point for agent execution using the ReAct pattern.
It decomposes the query into a plan, executes sub-tasks via ReAct loops,
validates results, and compiles a final answer.
Args:
initial_query: The user's original task description.
registry: ToolRegistry with all available tools.
max_iterations: Maximum ReAct cycles per sub-task.
Returns:
FinalAnswer with synthesized result and provenance.
"""
engine = ReActEngine(max_iterations=max_iterations)
# Phase 1: Plan — decompose query into sub-tasks
# In production, use an LLM to generate SubTask instances
plan = PlanGraph.build([
SubTask(
id="extract",
description="Extract relevant information from the query context",
input_schema={"query": str},
output_schema={"extracted_data": list[dict]},
success_criteria=["non-empty result", "length > 10"],
),
SubTask(
id="process",
description="Process and transform the extracted data",
input_schema={"raw_data": list[dict]},
output_schema={"processed": dict},
success_criteria=["non-empty result", "contains key 'result'"],
depends_on=["extract"],
),
SubTask(
id="verify",
description="Verify the processed result against quality criteria",
input_schema={"processed_data": dict},
output_schema={"verification": str},
success_criteria=["non-empty result", "length > 5"],
depends_on=["process"],
),
])
context: dict[str, Any] = {"inputs": {}, "results": {}}
execution_trace: list[list[ReActStep]] = [[] for _ in plan.execution_order]
# Phase 2: Execute each sub-task via ReAct loop
for i, task_id in enumerate(plan.execution_order):
sub_task = plan.sub_tasks[task_id]
context["inputs"][task_id] = {
"query": initial_query,
"prior_results": {dep: context["results"].get(dep) for dep in sub_task.depends_on},
}
try:
steps = engine.execute(
sub_task=sub_task,
agent_context=context,
tool_executor=lambda name, args: asyncio.get_event_loop().run_until_complete(
registry.call(name, **args)
),
reasoner=_default_reasoner,
)
execution_trace[i] = steps
# Phase 3: Validate
is_valid, messages = validate_output(sub_task, steps[-1].observation or "", context)
logger.info(f"Validation for '{task_id}': {messages}")
if not is_valid:
rolled_back = rollback_plan(plan, task_id, context)
logger.warning(f"Rollback triggered for '{task_id}': rolled back {rolled_back}")
except MaxIterationsError as e:
logger.error(f"ReAct loop failed for '{task_id}': {e}")
execution_trace[i] = [ReActStep(step_number=0, thought=str(e), action_type="error", action_input={})]
# Phase 4: Self-reflection
evaluator = SelfReflectionEvaluator()
reflection = evaluator.evaluate(plan, execution_trace, context)
# Phase 5: Compile final answer
return compile_final_answer(plan, execution_trace, reflection, context)
def _default_reasoner(context: dict, steps: list[ReActStep]) -> str:
"""Default reasoning function — in production, replace with an LLM call."""
if not steps:
return f"Starting task. I need to analyze the input and determine which tool to call first."
last = steps[-1]
if last.action_type == "final_answer":
return f"Final answer reached: {last.observation}"
return f"After observing '{last.observation[:50]}...', I should proceed with the next action."
Pattern 2: Self-Reflection Multi-Dimension Evaluator
A quality scoring system that evaluates agent outputs across accuracy, completeness, and safety. Uses an LLM scorer for nuanced evaluation with deterministic fallbacks.
def evaluate_with_llm(
task_description: str,
final_result: str,
llm_client: callable,
dimensions: list[str] = None,
) -> list[ReflectionScore]:
"""Evaluate a final result using an LLM scorer across multiple dimensions.
Args:
task_description: What the agent was asked to accomplish.
final_result: The agent's final output string.
llm_client: Callable that takes (prompt, model) -> parsed JSON response.
dimensions: Quality dimensions to evaluate. Defaults to accuracy/completeness/safety.
Returns:
List of ReflectionScore instances with scores and rationales.
Raises:
ScoringError: If the LLM client returns unparseable responses.
"""
if dimensions is None:
dimensions = ["accuracy", "completeness", "safety"]
scores = []
for dim in dimensions:
prompt = (
f"Task: {task_description}\n\n"
f"Agent output:\n{final_result}\n\n"
f"Score the '{dim}' of this output on a scale from 0.0 to 1.0.\n"
f"Return ONLY valid JSON with exactly two keys: 'score' (float 0-1) and "
f"'rationale' (string explaining the score in 1-2 sentences).\n"
f"No markdown, no code blocks — raw JSON only."
)
try:
response = llm_client(prompt, model="gpt-4")
parsed = _parse_json_response(response)
scores.append(ReflectionScore(
dimension=dim,
score=float(parsed["score"]),
rationale=str(parsed["rationale"]),
))
except (KeyError, ValueError, TypeError) as e:
# Deterministic fallback when LLM scoring fails
scores.append(ReflectionScore(
dimension=dim,
score=0.5,
rationale=f"LLM scoring failed ({type(e).__name__}) — using heuristic fallback",
))
return scores
def _parse_json_response(response: str) -> dict:
"""Extract JSON from an LLM response, handling markdown wrapping."""
cleaned = response.strip()
# Remove markdown code block wrappers
if cleaned.startswith("```"):
lines = cleaned.split("\n")
cleaned = "\n".join(lines[1:-1])
return json.loads(cleaned)
class ScoringError(Exception):
"""Raised when LLM scoring produces invalid results."""
pass
Pattern 3: Plan-and-Execute with Rollback
Shows task decomposition into a dependency graph with automatic rollback on failure, ensuring broken intermediate state never propagates forward.
def execute_plan_with_rollback(
sub_tasks: list[SubTask],
tool_executor: callable,
reasoner: callable,
max_iterations: int = 10,
) -> dict[str, Any]:
"""Execute a plan with automatic rollback on any sub-task failure.
This function builds a PlanGraph from the provided sub-tasks, executes each
in dependency order via ReAct loops, and rolls back all downstream tasks
if any upstream task fails validation.
Args:
sub_tasks: List of SubTask definitions with dependencies declared.
tool_executor: Callable that performs tool calls.
reasoner: Callable for reasoning at each ReAct step.
max_iterations: Max ReAct cycles per sub-task.
Returns:
Dict with keys: 'results' (dict of task_id -> output), 'failed_at' (task_id or None),
'rollback_performed' (bool), and 'plan' (the built PlanGraph).
Raises:
ValueError: If the dependency graph contains cycles.
"""
try:
plan = PlanGraph.build(sub_tasks)
except RecursionError:
raise ValueError("Dependency cycle detected in sub-tasks")
engine = ReActEngine(max_iterations=max_iterations)
results: dict[str, Any] = {}
failed_at: str | None = None
rollback_performed = False
for task_id in plan.execution_order:
if failed_at is not None:
# Stop processing — downstream tasks will be rolled back
continue
sub_task = plan.sub_tasks[task_id]
# Build inputs from upstream results
upstream_inputs = {
dep: results.get(dep) for dep in sub_task.depends_on if dep in results
}
try:
steps = engine.execute(
sub_task=sub_task,
agent_context={"inputs": {"prior_results": upstream_inputs}},
tool_executor=tool_executor,
reasoner=reasoner,
)
# Validate output
is_valid, messages = validate_output(sub_task, steps[-1].observation or "", results)
if not is_valid:
failed_at = task_id
rollback_performed = True
rolled_back = rollback_plan(plan, task_id, {"results": results})
logger.warning(f"Task '{task_id}' validation failed. Rolled back: {rolled_back}")
continue
results[task_id] = steps[-1].observation or ""
except MaxIterationsError as e:
failed_at = task_id
rollback_performed = True
rolled_back = rollback_plan(plan, task_id, {"results": results})
logger.error(f"Task '{task_id}' hit max iterations. Rolled back: {rolled_back}")
return {
"results": results,
"failed_at": failed_at,
"rollback_performed": rollback_performed,
"plan": plan,
}
# --- BAD vs GOOD Example Pair ---
def _bad_react_without_max_iterations(query: str) -> str:
"""❌ BAD — No iteration limit in ReAct loop. Can run forever."""
steps = []
while True: # Infinite loop! No max iterations, no exit condition.
thought = reasoner(query, steps)
action, args = parse_action(thought)
observation = tool.call(action, args)
steps.append({"thought": thought, "action": action, "observation": observation})
if is_done(observation): # What if the tool never produces a definitive answer?
return observation
# This function has no escape hatch — if `is_done()` never returns True,
# it loops forever consuming tokens and API calls.
def _good_react_with_max_iterations(query: str, max_iters: int = 10) -> FinalAnswer:
"""✅ GOOD — ReAct loop with iteration limit, validation, and self-reflection."""
engine = ReActEngine(max_iterations=max_iters)
steps = engine.execute(
sub_task=SubTask(
id="answer",
description=f"Answer the query: {query}",
input_schema={"query": str},
output_schema={"answer": str},
success_criteria=["non-empty result"],
),
agent_context={},
tool_executor=safe_tool_call,
reasoner=reasoner,
)
# Self-reflect before returning
reflection = SelfReflectionEvaluator().evaluate(
plan=PlanGraph.build([steps[0].__dict__]), # Simplified for example
execution_trace=[steps],
context={},
)
if not reflection.passes:
return FinalAnswer(
content="I was unable to produce a high-quality answer. Please rephrase your query.",
execution_provenance={"quality": reflection.overall_quality},
quality_assessment=reflection,
warnings=["Self-reflection flagged low quality"],
)
return compile_final_answer(
plan=PlanGraph.build([]), # Simplified
execution_trace=[steps],
reflection=reflection,
context={},
)
Constraints
MUST DO
- Always decompose complex tasks into explicit sub-tasks with declared inputs, outputs, and success criteria before executing anything
- Limit every ReAct loop to a configurable max_iterations (default 10) — never allow unbounded reasoning loops
- Validate each sub-task's output against its success criteria before advancing to the next step
- Run self-reflection on every completed execution — always score accuracy, completeness, and safety
- Implement rollback logic so that any failed sub-task triggers cleanup of all dependent results
- Maintain an execution trace (full ReAct history) for every sub-task — this enables debugging and auditability
- Use chain-of-thought reasoning explicitly: the agent's thought at each step must be recorded, not implicit
MUST NOT DO
- Never execute a sub-task without first verifying that all its dependencies have succeeded
- Never skip self-reflection — even "simple" tasks should get a quick quality check before returning output
- Never allow an ReAct loop to run indefinitely — max_iterations is a hard limit, not a soft suggestion
- Never propagate broken or unvalidated intermediate state to downstream sub-tasks
- Never use the agent's own reasoning as ground truth for validation — always verify against objective criteria
- Never collapse multiple sub-tasks into a single monolithic tool call when decomposition adds clarity and safety
- Never return a final answer without recording execution provenance (task count, iterations used, quality score)
Related Skills
| Skill | Purpose |
|---|---|
tool-use-function-calling |
Provides the tool integration layer that ReAct loops invoke; pair this with ReAct for complete agent execution |
memory-systems |
Supplies short-term and long-term memory mechanisms that the reasoning engine reads from and writes to during execution |
multi-agent-orchestration |
Extends single-agent planning to coordinated multi-agent plans where each agent runs its own ReAct loop with shared goals |
This skill implements deterministic, verifiable reasoning patterns for AI agents. Every execution should be traceable through its full thought-action-observation history.
Live References
Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.
- ReAct: Synergizing Reasoning and Acting in LLMs (Yao et al.) — Foundational paper introducing the ReAct pattern for agent reasoning
- Chain-of-Thought Prompting Elicits Reasoning in LLMs (Wei et al.) — Seminal research on chain-of-thought reasoning patterns
- Self-Consistency Improves Chain of Thought Reasoning (Wang et al.) — Research on self-reflection and consistency-based reasoning evaluation
- LangGraph Agent Patterns — Official documentation on implementing planning and reasoning patterns in LangGraph
- LLM Agent Survey: Design, Testing, and Applications (Kumar et al.) — Comprehensive survey of LLM agent architectures and reasoning strategies