Introduction

Agentic workflows move AI beyond one-shot prompting into iterative, autonomous problem-solving by letting agents plan, act, observe, and refine—much like a human tackling a complex task. This shift yields more reliable, adaptable, and goal-directed systems for real-world, multi-step problems. In this article I explain the five core agentic workflow patterns (Reflection, Tool Use, ReAct, Planning, and Multi-Agent), show how they combine, give practical implementation guidance, example architectures, and discuss trade-offs and evaluation strategies.

Table of contents

  • Introduction
  • What makes a workflow “agentic”?
  • Pattern 1 — Reflection (self-critique & revision)
  • Pattern 2 — Tool Use (APIs, search, computation)
  • Pattern 3 — ReAct (Reason + Act interleaving)
  • Pattern 4 — Planning (task decomposition & dependencies)
  • Pattern 5 — Multi-Agent (specialization & orchestration)
  • Combining patterns: common hybrid architectures
  • Implementation checklist & code examples
  • Evaluation, safety, and governance
  • Trade-offs and when to use each pattern
  • Conclusion

What makes a workflow “agentic”?

An agentic workflow is characterized by autonomy, iteration, and feedback loops: agents interpret a goal, choose actions (including calling tools), observe outcomes, and adapt without being scripted step-for-step[1][4]. Unlike traditional automation, agentic workflows use reasoning and context to decide next steps and refine behavior over time[4][1]. Core components are planning, execution, refinement, and human/system interface[3][5].

Pattern 1 — Reflection (self-critique & revision)

Why it matters

  • Reflection introduces an explicit evaluate-and-improve step so outputs are iteratively polished or corrected, improving accuracy and reducing hallucinations after initial generation[2][5].

How it works

  • After producing an output, the agent evaluates it against success criteria (factuality checks, schema validation, test cases), generates critiques, and issues targeted edits or re-runs subtasks[2][5].

Key techniques

  • Chain-of-thought style internal reasoning used for critique and error detection[2].
  • Automated checks: unit tests, schema validation, or retrieval-based verification against source documents[5].
  • Self-correct loop: produce → critique → rewrite → re-check until acceptance thresholds met[2][5].

When to use

  • Use reflection when accuracy, reliability, or compliance matter (e.g., legal summaries, code generation, finance).

Example (conceptual)

  • Generate a report → run fact-extraction and citation checks → agent finds unsupported claim → revise paragraph and attach citation → re-check.

Pattern 2 — Tool Use (APIs, search, computation)

Why it matters

  • Tools let agents go beyond language-only reasoning: fetch real-time data, run code, query databases, or execute tasks in external systems[1][4][5].

Tool categories

  • Retrieval tools: semantic search, knowledge bases, web browsing for up-to-date facts[1][5].
  • Execution tools: code interpreters, math engines, data pipelines, or RPA for side-effectful actions[2][5].
  • Integration APIs: CRM, ticketing, calendar, cloud services for real-world effects[1][4].

Design considerations

  • Tool specification: each tool needs a well-defined interface, input/output schema, and safety constraints[5].
  • Observability: capture tool outputs as structured observations the agent can reason about[2].
  • Layered permissions: restrict destructive tools and require human approval where needed[4].

Risks & mitigations

  • Incorrect tool usage can cause errors or unintended side effects; handle with sandboxing, rate limits, and post-action verification[4][5].

Pattern 3 — ReAct (Reason + Act interleaving)

Why it matters

  • ReAct interleaves explicit reasoning steps with actions, enabling adaptive decision-making: when an action fails or yields unexpected data, the next reasoning step adapts the plan accordingly[2][1].

How it works

  • Loop: observe → reason (explicitly articulated thoughts) → act (call tool or produce output) → observe results → repeat until goal attained[2].

Advantages

  • Adaptivity: diagnoses failures mid-flow and changes tactics.
  • Transparency: the reasoning trail helps debugging and trust.
  • Flexibility: suited to environments where actions reveal new information.

Implementational pattern

  • Log the agent’s internal “thoughts” and actions in structured records.
  • Limit chain-of-thought exposure in user-facing logs for safety, while retaining internal trace for developers[2][5].

Example (pseudo)

  • Think: “Need current stock price” → Act: call market API → Observe: price returned → Think: “Price > threshold, place order” → Act: call trading API.

Pattern 4 — Planning (task decomposition & dependency management)

Why it matters

  • For complex goals, planning decomposes work into ordered subtasks, identifies dependencies and parallelism, and assigns resources and tools—reducing failure from under-specified objectives[3][5].

Planning approaches

  • Top-down task decomposition: break goal into milestones and actions.
  • Dependency graph: encode prerequisites and parallelizable subtasks.
  • Adaptive planning: replan when observations invalidate assumptions[3][2].

Practical patterns

  • Generate a plan with estimated effort and required tools, then execute subplans iteratively and monitor progress[3][5].
  • Use hierarchical planners: a high-level planner issues subgoals, and lower-level agents (or the same agent in a different mode) execute them.

When planning shines

  • Long-horizon tasks (product launches, multi-step data analyses, complex research).
  • When coordinating many resources, enforcing order, or optimizing for time/cost.

Pattern 5 — Multi-Agent (specialization & orchestration)

Why it matters

  • Multi-agent systems split work among specialized agents (e.g., researcher, verifier, editor) coordinated by an orchestrator to improve throughput, modularity, and robustness[3][5].

Architectural roles

  • Orchestrator / conductor: decomposes tasks, assigns subtasks, merges outputs, reconciles conflicts.
  • Specialist agents: focused on a narrow skill (retrieval, synthesis, code writing, verification).
  • Mediator agents: resolve inconsistent results, negotiate trade-offs, or ensure safety.

Coordination strategies

  • Sequential pipeline: one agent’s output feeds the next.
  • Blackboard/shared memory: agents post observations and read others’ results.
  • Negotiation/contract net: agents bid for subtasks based on capability and load.

Benefits and costs

  • Benefits: parallelism, clearer ownership, easier testing of subcomponents.
  • Costs: communication overhead, complexity of orchestration, new failure modes (deadlocks, conflicting outputs)[3][5].

Combining patterns: common hybrid architectures

Agents rarely use a single pattern in isolation. Common combinations include:

  • Planning + ReAct + Tool Use: Planner produces a decomposition; within each subtask the agent uses ReAct cycles to call tools and adapt[3][2][5].
  • Reflection + Tool Use: After tool-based retrieval or computations, reflect to validate and correct outputs before finalizing[2][5].
  • Multi-Agent + Reflection: Specialist verifier agent performs reflection on outputs from a producer agent, improving safety and quality[3][5].

Example architecture (high-level)

  • User goal → Orchestrator creates plan → Worker agents execute steps (use tools) with ReAct loops → Verifier agent reflects and validates → Orchestrator merges and returns result.

Implementation checklist & code examples

Checklist (design & infra)

  • Define goal spec and success criteria (measurable).
  • Select base LLM(s) and fine-tune or prompt-tune for reasoning/tool usage.
  • Implement robust tool interfaces with typed I/O.
  • Build observability: action logs, agent reasoning traces, & metrics.
  • Safety layers: permissioning, human-in-the-loop gates, sandboxing.
  • Testing harness: unit tests for subtasks, integration tests for flows.
  • Monitoring & retraining loop: collect failure cases and refine prompts/models.

Minimal ReAct loop (Python pseudo-code using an LLM and a tool registry)

# Pseudo-code: ReAct loop
def react_agent(goal, max_steps=10):
    state = {"goal": goal, "history": []}
    for step in range(max_steps):
        prompt = render_prompt(state)
        lm_output = llm.generate(prompt)
        thought, action = parse_react(lm_output)
        state["history"].append({"thought": thought, "action": action})
        if action["type"] == "finish":
            return action["result"]
        tool_output = call_tool(action["tool"], action["args"])
        state["last_observation"] = tool_output
    raise RuntimeError("Max steps exceeded")

Notes

  • render_prompt should include goal, observations, and a template encouraging explicit thoughts and actions.
  • parse_react extracts structured action from LLM text (use JSON output or a constrained grammar).
  • call_tool must validate inputs and sandbox side effects.

Verifier pattern (reflection)

  • Run an automated verifier that checks outputs against evidence (retrieval), test cases, or schema validators; if checks fail, push back a critique to the agent for revision[2][5].

Evaluation, safety, and governance

Evaluation metrics

  • Task success rate (meets goal/spec).
  • Number of actions and time to completion (efficiency).
  • Robustness: ability to recover from failed actions or noisy tools.
  • Interpretability: clarity of reasoning trail for audits[2][4].

Safety controls

  • Least-privilege tool access and human approval for high-risk tools[4].
  • Rate limiting, transaction logs, and immutable audit trails.
  • Verifier agents and human-in-the-loop gates for decisions with legal, financial, or safety consequences[4][5].

Governance practices

  • Define allowed behaviors and unacceptable actions in policy.
  • Maintain provenance: every fact or external call should be traceable to source.
  • Regularly review failure logs and update prompts, tool constraints, or models.

Trade-offs and when to use each pattern

  • Reflection: use when correctness and compliance are critical; costs are extra latency and compute.
  • Tool Use: essential for real-time data and side effects; increases system complexity and attack surface.
  • ReAct: best for exploratory or information-gathering tasks where actions yield new info; requires structured action formats.
  • Planning: applies to long-horizon problems; upfront planning reduces wasted work but can be brittle if the world changes quickly.
  • Multi-Agent: use when specialization, parallelism, or modularity are priorities; introduces orchestration complexity.

Choose minimal patterns to satisfy requirements and only add complexity when needed.

Conclusion

Agentic workflow patterns—Reflection, Tool Use, ReAct, Planning, and Multi-Agent—are pragmatic building blocks for turning LLMs and AI models into dependable, goal-driven systems. Combining these patterns thoughtfully yields systems that can plan, act, learn from outcomes, and coordinate work across specialists, enabling AI to handle complex, real-world tasks more reliably than one-shot prompting. Adopt rigorous tooling, observability, and governance to manage the added complexity and risks.

Important note: start simple, measure, and iterate—just like the agents you build.

Resources (select further reading)

  • Atlassian: Understanding AI Agentic Workflows for practical descriptions of perception, decision-making, and continuous feedback[1].
  • ByteByteGo: Deep dives into ReAct, planning, and pattern examples for agentic systems[2].
  • Vellum AI: Architectural guide to agent workflows, components, and multi-agent examples[3].
  • GoodData: Practical steps for building agentic workflows and business use cases[4].
  • Weaviate: Patterns and technical details on task decomposition and reflection[5].