Planner–Executor excels when goals are clear and sequences can be pre‑computed. ReAct shines when the path is uncertain and observations guide the next step. Real applications need both: a coarse plan for direction, and an iterative loop for discovery and correction.
Why Hybrid Wins
- Direction + Discovery: Upfront planning reduces cost and chaos; reactive loops adapt to surprises.
- Speed + Safety: Deterministic executors deliver throughput; reflective checkpoints prevent silent failures.
- Interpretability: Plans and scratchpads create an auditable trail of decisions, actions, and evidence.
Common Hybrid Patterns
Planner → Micro‑ReAct
Generate a plan once; each step runs inside a bounded ReAct loop with tool use and guardrails.
- Budgets per step (tokens, time, tool calls)
- Human‑in‑the‑loop for risky actions
- Structured outputs validated by schema
ReAct with Plan Checkpoints
ReAct iterates freely but pauses at milestones to synthesize progress and (re)plan the remaining path.
- Periodic summaries and goal re‑alignment
- Abort or branch if assumptions fail
- Cache successes; avoid repeated dead‑ends
Reflective Final CoT
After execution, a final Chain‑of‑Thought review checks completeness, consistency, and evidence linkage.
- Ground reasoning strictly in prior actions/observations
- Detect gaps; request follow‑up actions if needed
- Produce citations and an executive summary
Hierarchical Teams
Coordinator agents (Planner/Analyst) manage specialist workers (Executors/Tools) with escalation rules.
- Role prompts and clear interfaces
- Priority queues and retries with idempotency
- Telemetry for evaluation and tuning
Design Principles
- Guardrails: policy checks, sandboxing, dry‑run/approve flows, and audit logs.
- Evaluation: golden tasks, regression suites, offline replay, and live metrics.
- Budgets: constrain steps and escalate on over‑spend or uncertainty.
- Structure: JSON schemas; strict validators at plan/step/final outputs.
Build It Today
You can compose these patterns with modern frameworks:
- LangChain / LangGraph: explicit state machines and control flow
- AutoGen: multi‑agent conversations with role prompts and tools
- LlamaIndex: data‑centric agents and RAG pipelines
For hands‑on examples, see Practical Examples and the blog on Dual Reasoning.