Resources/Agentic Architecture Atlas

7 real patterns

Agentic Architecture Atlas: 7 Real AI System Patterns

Every entry below is a system we actually run, not a survey of other people's architectures. Each diagram is reused directly from the page that documents it, or built from the exact steps published there. Filter by category, read the plain-language walkthrough, then follow the link to the full breakdown.

Multi-Agent Systems

Multi-Agent Swarm (the /go Build Cycle)

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This is the exact graph our own build system runs on every task: a planner node fans work out to scoped worker nodes, each worker writes only inside its own lane, and everything routes through a shared adversarial-review checkpoint before it ships. A failed review loops back to planning instead of forcing the same broken output through. All of it reads and writes one shared tracking layer, plain files, not a database, so any node can see what any other node already decided.

When to use it

Reach for a swarm when two or more specialized roles need to hand off work to each other, or when independent chunks of a task can run in parallel without touching the same files. A single agent retrying against one pass/fail check does not need this, that is a loop, not a graph.

Read the graph engineering breakdown

Governance & Deployment

The FIRE Loop (Find, Instrument, Rollout, Evolve)

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FIRE is the operating loop we run on every AI deployment, not a one-time rollout plan. Find means sitting with the people who do the work today instead of trusting the documented version of it. Instrument turns the messy, non-deterministic parts into evidence: a golden dataset, pass-rate tracking, and a full audit trail. Rollout ships behind a real human review gate with autonomy raised gradually, and Evolve pulls every override a person makes back into the next Find cycle, so the system gets sharper instead of going stale.

When to use it

Use this loop for any AI deployment that needs to keep improving after launch, especially where governance, an audit trail, or a documented escalation path matters to the buyer. A one-off script that never touches production again does not need the full loop.

Read the FIRE framework

Governance & Deployment

The Closed-Loop Software Factory

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This is the shape behind every harnessed AI-coding loop we run at scale: a queue of intents feeds a harness (tools, retries, context), automated checks catch what can be caught cheaply, and everything else routes through one review gate, the single step in the loop that does not scale by adding more compute. Monitoring after deploy feeds the next cycle's intent, closing the loop instead of ending at ship.

When to use it

This pattern earns its complexity once you are running many agent loops at once and need one consistent gate deciding what actually reaches production, not for a single one-off script running once.

Read the software factory breakdown

Data & Retrieval

RAG Pipeline with Guardrails

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This is our real three-step engineering process for RAG: intelligent data chunking that preserves semantic structure instead of cutting text blindly, hybrid search orchestration that combines keyword search with vector embeddings so both technical jargon and natural-language intent get matched, and a continuous RAGAS evaluation loop that measures faithfulness and relevance before anything ships. The goal is a system grounded in a company's real documentation, not a model guessing from its training data.

When to use it

Reach for this when an AI system needs to answer from a private, changing knowledge base and hallucinations are not acceptable, internal support bots, compliance Q&A, or any assistant grounded in your own documents. A static FAQ or a small, rarely-changing knowledge base usually does not need the full retrieval and eval loop.

See our RAG pipeline architecture service

Customer & Revenue Agents

Support Agent Triage Loop

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Three specialized agents plug into an existing helpdesk over webhook rather than replacing it: a triage agent tags and routes each ticket in under 2 seconds, a draft agent writes a reply in the brand's voice with citations pulled from a vector index of the help docs, and an approval agent auto-sends replies above a confidence threshold while holding lower-confidence ones for a human. Billing and other specialized topics route straight to a dedicated queue instead of through the draft step.

When to use it

This fits a support team already running a standard helpdesk (Intercom, Zendesk, Help Scout) that wants to handle repeat, answerable tickets automatically while keeping a human in the loop for anything the model is not confident about. It is not a fit for a team that wants full replacement with zero human review.

Read the customer support workflow

Customer & Revenue Agents

Lead Qualification Router

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Four steps, each under 30 seconds: enrich pulls company, team size, tech stack, and recent fundraising from an enrichment tool, score has a Claude agent rate ICP fit from 1 to 10 with a one-sentence reason, route sends high scores straight to an account executive's Slack, mid scores to a digest, and low scores to a nurture sequence, and backfill pushes the score and reasoning back into the CRM so the AE has context before they ever open the record.

When to use it

Use this when inbound lead volume is high enough that manual triage creates real response-time lag, and there is a clear enough ICP definition to give a scoring agent concrete disqualifiers and examples. A low-volume pipeline that a rep can qualify by hand in minutes does not need this yet.

Read the lead qualification playbook

Workflow Automation

n8n x Claude Production Builder

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This is how we build n8n automations meant to survive past week two: map the process with the person who runs it today, including the exceptions nobody mentions in the first meeting, list every failure mode before writing the happy path, make every run safe to retry with dedupe keys and checked writes, wire Slack alerts plus a heartbeat so silent failures get caught, and hand over a runbook with a live session where the client's team breaks and fixes the workflow with us watching. Claude handles the nodes that need judgment; n8n handles the glue.

When to use it

This fits a real, stable business process running often enough to justify the build, and where someone on the client's team will actually own the n8n instance afterward. It is a poor fit for a process that changes weekly or runs a few times a month.

Read the n8n workflow builder guide

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Stop Reading Diagrams. Have One Built.

Tell us the workflow. We will tell you honestly which of these patterns fits, and place a forward deployed engineer to build it if you decide to move forward.

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Recommended services

Forward Deployed Engineers

The embedded engineers who build and run architectures like these inside your stack.

AI Agent Development

Custom agents and internal knowledge bases for complex, multi-step work.

RAG Pipeline Architecture

The retrieval pipeline behind the RAG pattern on this page.

AI Automation Agency

Strategy, build, and maintenance for the n8n and agent workflows on this page.

FAQ

Agentic Architecture Atlas Questions

What is the Agentic Architecture Atlas?+

It is a filterable gallery of the actual agent and automation architectures AY Automate runs in production, each shown as a diagram with a plain-language walkthrough and a link to the full breakdown. It is not a survey of architectures other companies use, every entry here is a pattern we built and ship.

Are these real client builds or theoretical patterns?+

Real. Each diagram is either reused directly from the page that documents that system, or built from the exact steps described on that page. None of the diagrams or explanations invent a client story or a metric that isn't already published elsewhere on this site.

How do I pick the right pattern for my use case?+

Start from what the workflow actually needs. A single agent retrying against one check is a loop, not a graph. Multiple specialized roles handing off work to each other is a swarm. Grounding answers in private documents is RAG. Read the "when to use it" note on each entry, then follow the link through to the full breakdown before committing to an approach.

Can I reuse these diagrams?+

The diagrams describe our own systems and the underlying patterns are common architecture concepts (loops, graphs, retrieval pipelines), not proprietary trade secrets. If you reference one in your own writing, a link back to this page is appreciated but not required.