How to Build AI Agents: A Step-by-Step Implementation Path from Prompt Chaining to Multi-Agent Systems

Start with the smallest thing that can act: one LLM call that can choose a tool, observe the result, and decide whether to call another. That loop — model, tools, state — is a working agent. Everything else on this page is an upgrade you add only when the simple loop fails at a specific job. The 21 agentic design patterns catalogued by learnagenticpatterns.com (based on Antonio Gullí's 21 Agentic Design Patterns) give you the upgrade menu, each mapped to a familiar software architecture equivalent.

The minimum viable agent loop

Three elements, no framework required:

• Model — the LLM that decides what to do next.
• Tools — functions the model can invoke (search, database query, API call, file write).
• State — the running conversation plus any structured data you carry between turns.

The loop: send the task and tool definitions to the model → if it returns a tool call, execute it and append the result → repeat until it returns a final answer or you hit a step limit. Set that step limit on day one; an agent with no ceiling will burn tokens in a loop.

Verify it works: give it a task that requires exactly one tool call ("what's the current status of order 4471?"). If it calls the tool and answers from the result, the loop is sound. If it answers from memory instead, your tool descriptions are too vague.

Decompose before you orchestrate

Most "the agent is unreliable" problems are really "the task was too big for one call." The foundational fix is Prompt Chaining (pattern 01, mapped to Pipe & Filter): break the task into a linear sequence of smaller LLM calls, where each step's output becomes the next step's input.

Example: instead of "write a competitive analysis," chain it — extract the competitor list → for each competitor, summarize their positioning → synthesize the comparison → draft the report. Each step is testable in isolation, and when output is wrong you know which link broke.

Add Routing (pattern 02, ≈ Load Balancer / API Gateway) when different inputs need different handling. A router classifies the incoming request by intent and complexity, then delegates to the handler optimized for that case — a cheap model for simple lookups, a specialized prompt for billing questions. Add Parallelization (pattern 03, ≈ Scatter-Gather / MapReduce) when subtasks are independent: run several drafts, fact-checks, or votes simultaneously, then aggregate.

Make it reliable: reflection, planning, memory

Reflection (pattern 04, ≈ Unit Testing / TDD Loop) has the agent critique its own output against criteria, then revise. It's the cheapest reliability win available — but only if the critique is specific ("does this cite a source for every claim?") rather than generic ("is this good?").

Planning (pattern 06, ≈ Workflow Orchestration / Saga) makes the agent produce an explicit step list before executing. This matters most for multi-step tasks where a wrong early decision cascades. You get a reviewable artifact before any side effects happen.

Memory Management (pattern 08, ≈ Database / Caching / Session State) covers what persists: short-term context within a session, long-term facts across sessions, and cached results you don't want to recompute. Knowledge Retrieval / RAG (pattern 14, ≈ Database Query / Search Index) is the read path — the agent queries an index instead of relying on parametric memory. Keep these separate: memory is what the agent learned or was told; retrieval is what it looks up on demand.

Handle failure and high-stakes actions

Three patterns turn a demo into something you can run unattended:

• Exception Handling & Recovery (pattern 12, ≈ Try-Catch / Circuit Breaker) — define retry, fallback, and abort behavior per tool. A failing search API should degrade, not crash the run.
• Human-in-the-Loop (pattern 13, ≈ Manual Approval Gates) — insert an approval step before irreversible actions: sending email, writing to production, spending money. Decide the gate by blast radius, not by how confident the model sounds.
• Goal Setting & Monitoring (pattern 11, ≈ Health Checks / APM) — track whether the agent is still making progress toward the stated goal, and stop it when it isn't.

When to go multi-agent

Multi-Agent Collaboration (pattern 07, ≈ Microservices Architecture) and Inter-Agent Communication / A2A (pattern 15) are the last upgrades, not the first. Split into multiple agents only when you have distinct roles with genuinely different tools or prompts, and when a single agent's context has become the bottleneck. Each agent boundary adds a communication failure mode; a well-chained single agent beats a poorly coordinated team.

A practical build order

1. Single loop with one tool. Verify the tool actually gets called.
2. Chain the steps. Test each link independently.
3. Add routing when request types diverge.
4. Add reflection and planning where output quality or sequencing matters.
5. Add memory and RAG when the agent needs context it wasn't trained on.
6. Add exception handling, approval gates, and monitoring before any real side effects.
7. Go multi-agent only when a single agent provably can't hold the job.

The curriculum at learnagenticpatterns.com presents these as interactive practice problems — "like LeetCode, but for AI agents" — with pseudocode, software-engineering mappings, and production notes. Seven patterns and the first product-manager module are open without an account; a free account unlocks the rest.