Self-Correcting ReAct Agent
Autonomous Agent Control Loop Framework Built From Scratch with Budget-Capped Backtracking
// 01. THE PROBLEM
Standard LLM tool-calling fails silently or enters infinite loops when web scraping tools return malformed output, missing arguments, or rate-limit errors.
// 02. SYSTEM ARCHITECTURE & PIPELINE FLOW
Goal Deconstruction & Tool Routing
Decomposes complex user research queries into atomic tool invocation steps (Web Scraper, Calculator, Fact Verifier).
ReAct Loop & Tool Execution
Executes tool calls and streams tool outputs into a bounded working memory stack.
Critique Gate & Reflection
Independent Evaluator model audits tool outputs for malformed data or hallucinated citations.
Budget-Capped Backtracking
If verification fails, rewinds state stack, reformulates prompt, and attempts alternative execution paths.
// 03. TECHNICAL APPROACH & DECISION MATRIX
01Modular ReAct Architecture From Scratch
Custom Planner, Orchestrator, Tool Router, Working Memory, and Evaluator
Building the execution loop from scratch provided full visibility into agent state transitions, token budget consumption, and custom error handling.
Framework Wrappers (LangChain AgentExecutor)
High-level abstractions hid internal memory stack states, making deterministic state backtracking and custom retry logic impossible.
02Budget-Capped State Backtracking
State Backtracking & Prompt Reformulation Loop
State backtracking allows the agent to rewind its memory stack to the last known good state and attempt an alternative action path.
Naive Retry Loops Without State Mutation
Retrying the exact same tool call without prompt modification caused infinite loop execution burns.
// 04. CORE ARCHITECTURAL LOGIC
State backtracking and prompt reformulation loop preventing infinite agent execution loops.
123456789101112131415161718192021async def execute_agent_loop(task: AgentTask, max_steps: int = 5) -> TaskResult: memory_stack = [task.initial_prompt] step_count = 0 while step_count < max_steps: thought, action, tool_args = await planner.next_step(memory_stack) try: observation = await tool_router.dispatch(action, tool_args) verified = await evaluator.verify(observation) if verified.is_valid: return TaskResult(success=True, answer=observation) # ReAct reflection & prompt reformulation upon verification fault memory_stack.append(f"Fault: {verified.reason}. Reformulating plan.") except ToolExecutionError as err: # Budget-capped state backtracking to last verified checkpoint memory_stack = memory_stack[:step_count] memory_stack.append(f"Tool Error: {err}. Routing fallback tool.") step_count += 1 return TaskResult(success=False, error="Budget cap limit reached (5 steps)")
// 05. TRADE-OFFS & HONEST REFLECTION
Adding reflection and evaluator validation steps increases total LLM calls per query by ~30%, but completely prevents hallucinated or unverified final answers.