Agent‑style artificial intelligence—systems that can reason, plan and act on our behalf—has evolved fast, but its underlying design wisdom is scattered across dozens of research threads. Drawing on recent papers, the following tour distils ten recurring patterns that practitioners can adopt, mix or refine when building their own autonomous or semi‑autonomous agents.

1. ReAct: Think, Act, Observe, Repeat

If you’ve ever watched someone teaching a toddler to walk, you’ll recognize the core of ReAct: try something, see what happens, and adjust. In AI land, ReAct tells a single language model to

  1. think (generate a mini chain‑of‑thought),

  2. act (call a tool or function),

  3. observe (read back the tool’s output),
    and loop until you get an answer. It beats simple prompting on tasks where you need multiple steps—just don’t be surprised if it sometimes goes in circles if it can’t invent fresh thoughts.

When to use: quick prototypes, tasks with a handful of tools, when you don’t need full multi‑agent complexity.

2. Reflexion: Auto‑Critique for Sticky Problems

Imagine you’ve been stumped on a puzzle for ten minutes. You’d probably stop and ask yourself, “Why am I stuck?” That’s Reflexion in a nutshell. After a few ReAct iterations fail, you loop in a critic—often the same LLM—to examine errors and propose a new approach. It cuts down on mindless retries and helps the agent learn from its own mistakes.

Pro tip: Reflexion shines on longer tasks where repeated random tries just waste time and tokens.

3. Language Agent Tree Search (LATS): Branch and Backtrack

Sometimes you need a full decision‑tree search. LATS lets the agent spawn multiple parallel “what if” branches in natural English, prune the losers, and backtrack when a branch leads nowhere. The result? Higher‑quality plans at the cost of more compute. Just remember to cache your evaluations—nobody wants to recompute the same dead ends.

4. Vertical Leadership: One Boss, Many Specialists

What if you had a project manager and a dozen experts each tackling one piece of your task? That’s vertical multi‑agent: a “leader” agent outlines the workflow, while specialized sub‑agents—perhaps a code writer, a data fetcher, a summarizer—execute each step. Letting a human lead the team gave the best results, but a well‑prompted LLM leader works too.

Checklist:

  • Write a concise brief for your leader.

  • Give each specialist just a few tools.

  • Filter their inputs so they see only what they need.

5. Horizontal Debate & Voting: Wisdom of the (Agent) Crowd

No single mind has all the answers, so why not let three or five argue it out? In horizontal multi‑agent setups, each agent proposes a solution:

  • Voting: pick the majority.

  • Debate: agents exchange counterarguments until one wins.

  • Role‑based: each agent has a fixed role (writer, editor, tester).

MetaGPT and AgentVerse both leverage these vibes to outperform solo agents on code and reasoning benchmarks.

6. Dynamic Team Construction: Recruit, Evaluate, Retire

Just like you wouldn’t keep every team member on every project, dynamic recruitment adjusts your roster on the fly. Based on progress metrics or skill matches, the leader agent invites new experts or shows others the door—trimming chatter and focusing firepower where it’s needed most.

How to implement:

  • Tag each agent with a skill‑vector.

  • At each phase, run a similarity search between the current need and your agent registry.

7. Router Agents for RAG: Know When to Call the Specialist

If your question ranges across politics, science and cooking, you don’t want one giant “search” to spit back a jumbled mess. A RAG Router first figures out, “Is this a legal question? Or medical? Or trivia?” and then dispatches the query to the right domain index. Downstream, another agent stitches the contexts into one cohesive answer.

Why you need it:

  • More accurate retrieval.

  • Reduced hallucinations.

  • Easier compliance per domain (e.g. HIPAA vs. open web).

8. Orchestrated Multi‑Agent Systems with Guardrails

Take your router and tack on an orchestration layer plus a “guardrail” agent that checks every tool call for style, policy or privacy violations before the final output goes live. The conductor oversees specialist agents—think coder, analyst, translator—and a safety agent runs red‑team checks, flags risky content, or enforces data‑handling rules.

Implementation tip:

  • Keep a central ledger of every action so you can rewind or audit at will.

9. Human‑in‑the‑Loop (HITL) Checkpoints

Full autonomy is cool… until things go off the rails. Solution? Sprinkle in HITL checkpoints at strategic milestones. For example an AI hardware verification engineer, won’t push code until a human signs off on the generated assertions. You can configure iteration limits or timeouts so that the system politely asks for a human assist before it spirals.

10. Safety‑First Distributed Agents (DAWN)

When your agents roam the open web, you need a bouncer at the door. The DAWN framework wraps every external call in a Gateway Agent that vets each new tool, model or API for safety, compliance and security. Gateways can automatically run vulnerability scans, enforce privacy redactions, or block suspicious actions—scaling your guardrails as your agent fleet grows.

Why it matters:

  • Keeps your core logic clean and focused.

  • Makes security audits far simpler.

  • Avoids teaching every agent about every other agent’s quirks.

Bringing It All Together

These patterns are the building blocks of reliable, maintainable agentic AI. Agent design is no longer magic. With these patterns, you have a roadmap to craft agents that actually get things done—without constantly pinging you for instructions or hallucinating fantasy APIs. Happy architecting!