AI Agent Architecture Explained for Decision-Makers
The six layers of a production AI agent, what happens when a request arrives, single vs multi-agent, and where the guardrails live.
- HCEO and Co-Founder at Limbani Softwares
Published
Quick answer
A production AI agent has six layers: a channel where requests arrive, an orchestration layer that runs the loop, a model that plans and writes, tools the model may call within permissions, retrieval and memory that supply context, and observability with logs, evaluation and cost tracking. Human approval sits in front of risky actions. Decision-makers do not need to read code to judge an agent; they need to see these layers and ask what each one is allowed to do.
Key takeaways
- Six layers: channel, orchestration, model, tools, retrieval and memory, observability.
- Permissions and approvals are architecture decisions, not afterthoughts.
- Single agent for most tasks; multi-agent only when the work naturally splits.
- Ask vendors to show these layers for a real agent before you buy.
The six layers of an AI agent architecture#
| Layer | What it does | Questions to ask |
|---|---|---|
| Channel | Where requests come from: web, WhatsApp, voice, email, internal tools | Which channels, and how is identity checked? |
| Orchestration | Runs the loop: call the model, execute tools, retry, hand off | What runs it? How are failures handled? |
| Model | Plans the next step, writes responses | Which model, can it be swapped, is data used for training? |
| Tools | Named functions on your systems | What may it read and write? Which actions need approval? |
| Retrieval and memory | Documents and records pulled in at run time; task state | Where is data stored, in which region? |
| Observability | Logs, evaluation, cost tracking, alerts | How do we know it is working next month? |
What happens when a request arrives#
- 1. Intake. The channel receives the request and the orchestration layer creates a task.
- 2. Context. Retrieval fetches relevant records and documents.
- 3. Plan. The model proposes the next step and which tool to call.
- 4. Execute. The tool runs with its permissions; results return to the model.
- 5. Check. Guardrails validate the output; risky actions wait for a person.
- 6. Complete and log. The result is delivered and every step is recorded.
Single agent or multi-agent?#
Most business tasks are handled best by one agent with a small set of tools. Multi-agent designs, where one agent delegates to specialists, add coordination cost and failure points; use them only when the work naturally splits into independent parts, such as research followed by drafting followed by review.
Where the guardrails live#
- Permissions. Each tool has a scope: read-only, write with approval, write freely for low-risk fields.
- Validation. Inputs are checked for injection attempts; outputs are checked against a schema.
- Approvals. Money, record changes and customer contact wait for a person until trust is earned.
- Limits. Maximum steps per task, cost per task and rate limits per channel.
- Fallbacks. When the agent cannot help, the task goes to a person with full context.
Reference example#
In the AI receptionist MVP for a salon chain, the channel is phone and WhatsApp, n8n provides the orchestration, the model handles conversation and intent, tools cover availability checks, bookings and CRM updates, retrieval answers FAQs from a knowledge base, and staff handoff is the fallback. The same layering applies to support, operations and sales agents.
Using this when you evaluate a vendor#
Ask a prospective AI agent development company to walk you through these six layers for an agent they have built. If they cannot name the tools and permissions, the evaluation method and the cost per task, keep looking. Our guide to choosing an AI agent development company lists the questions.
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Tell us what you are building. Our team will reply with practical next steps, a scope-based estimate and a realistic timeline.
