AI Agent Architecture | Multi-Agent Claude Systems

At a glance

What is AI Agent Architecture?

AI Agent Architecture is the design and build of multi-agent systems where specialised Claude-based agents collaborate on larger workflows. Each agent has a defined role, its own memory, its own skills, and clear handoff rules with the other agents. Built on Claude Code and the Claude API, these systems handle tasks too complex for a single prompt or single-purpose automation.

What's included

Every engagement ships with.

Agent system design document
A written blueprint of every agent in the system: its role, its inputs, its outputs, its memory, and how it interacts with the others. Signed off before any build work starts.
Specialised Claude Code agents
Each agent built as a Claude Code skill or sub-agent with its own instructions, tools, and scope. Typically 2-6 agents per system.
Shared memory layer
A persistent memory system (Obsidian vault, SQLite, or file-based) so agents remember clients, history, past decisions, and each other's work.
Scheduled + triggered execution
Agents run on schedules (Monday briefing), triggers (new input), or on-demand via natural language. All three patterns supported in the same system.
Orchestration logic
Clear rules for which agent handles what, when agents hand off to each other, and when a human is looped in. Built into the system, not documented separately.
Operator documentation
A non-technical manual for the humans using the system: how to trigger agents, how to inspect their work, how to add new clients or extend the system as it grows.

Fit Check

Is this service right for you?

Both columns matter. Read them before booking.

This fits if…

Your workflow has multiple distinct stages (research → draft → review → publish) and a single prompt cannot handle them all.
The same agents need to remember clients, projects, or history across sessions.
Different people on your team need different types of output from the same underlying data.
You want the system to be extendable: adding a new agent or a new client should take hours, not weeks.

This is not for you if…

A single-prompt automation would actually be enough for your workflow (I will tell you).
You are not ready to commit to Claude as the core model. Agent systems are tightly coupled to one vendor.
The workflow runs once a year. Agent systems are built for frequent, repeating operations.
You want a no-code tool you can wire up yourself. These systems live in code and files.

Process

How it actually runs.

01

Architecture session

A deep-dive working session (2-3 hours, paid) where we design the agent system together: which agents, which memory, which handoffs. You leave with the design document.

02

Skeleton build

The agent framework is stood up with placeholder logic so you can see the shape of the system before any real work is done.

03

Agent build

Each agent is built and tested individually against real inputs. Voice, memory, outputs all calibrated before the agents are wired together.

04

Integration

Agents wired into the shared memory layer and orchestrated. Scheduled triggers activated. Dry runs start.

05

Go live + training

System handed over with a full operator walkthrough. You learn how to run it, extend it, and troubleshoot it. 14-day warranty starts.

Stack

Built with.

Claude Code Claude API Claude Sonnet Obsidian (memory) SQLite Python Agent Skills Sub-agents

Live Example

See this in production.

A real system running right now, built on this exact service.

Case Study · Live

AI Social Media Operating System

A real 4-agent Claude Code build for a social media manager running 4-7 clients. Weekly briefing agent, content pipeline agent, client report agent, and research agent, all sharing a memory layer and running on schedule. 70-80% of manual work automated without losing brand voice.

Read Case Study

FAQ

Before you book.

What is the difference between a multi-agent system and a regular automation?

A regular automation runs one task end to end (input → process → output). A multi-agent system has multiple specialised workers collaborating on a larger workflow. Each agent has its own role, memory, and scope. They hand off to each other. One triggers another. They share state. Agent systems are worth building when the workflow is too complex or too varied for a single-purpose pipeline to handle cleanly.

Do you use Claude Code or custom Python for agents?

Both, depending on the scope. Claude Code is the default because it ships with sub-agents, skills, file access, and memory out of the box. For agents that need tight integration with external APIs or custom data pipelines I drop into Python + Claude API. Often a system mixes both: the orchestration layer is Claude Code, specific heavy-lifting agents are Python.

How long does a multi-agent build take?

Four to eight weeks for most client systems, depending on the number of agents, the complexity of the memory layer, and how many external integrations are involved. Architecture session happens in week one, first agents running in week two, full system integrated by week four at the earliest.

Can the system handle multiple clients / tenants?

Yes. The Camille system (see the case study below) runs 4-7 clients simultaneously with per-client memory, per-client brand voice, and per-client output. Multi-tenancy is designed in from the start if your workflow needs it.

What if I want to add a new agent after the system is live?

Every system is built to be extendable. Adding a new agent follows the same pattern as the original build: design its role, give it memory, wire its handoffs. I offer optional retainers for ongoing agent additions, or you can use the handoff documentation to have any developer extend it.

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