Agent Builder
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
Applicability
Treat the architecture and orchestration guidance as provider-agnostic. The bundled Python starter code currently uses the Anthropic SDK and an Anthropic model configuration; adapt that implementation layer when using another provider. Do not mistake the example client for a required agent architecture.
The Core Philosophy
The model already knows how to be an agent. Your job is to get out of the way.
An agent is not complex engineering. It's a simple loop that invites the model to act:
LOOP:
Model sees: context + available capabilities
Model decides: act or respond
If act: execute capability, add result, continue
If respond: return to user
That's it. The magic isn't in the code - it's in the model. Your code just provides the opportunity.
The Three Elements
1. Capabilities (What can it DO?)
Atomic actions the agent can perform: search, read, create, send, query, modify.
Design principle: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
2. Knowledge (What does it KNOW?)
Domain expertise injected on-demand: policies, workflows, best practices, schemas.
Design principle: Make knowledge available, not mandatory. Load it when relevant, not upfront.
3. Context (What has happened?)
The conversation history - the thread connecting actions into coherent behavior.
Design principle: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
Agent Design Thinking
Before building, understand:
- Purpose: What should this agent accomplish?
- Domain: What world does it operate in? (customer service, research, operations, creative...)
- Capabilities: What 3-5 actions are essential?
- Knowledge: What expertise does it need access to?
- Trust: What decisions can you delegate to the model?
CRITICAL: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.
Progressive Complexity
Start simple. Add complexity only when real usage reveals the need:
| Level | What to add | When to add it |
|---|
| Basic | 3-5 capabilities | Always start here |
| Planning | Progress tracking | Multi-step tasks lose coherence |
| Subagents | Isolated child agents | Exploration pollutes context |
| Skills | On-demand knowledge | Domain expertise needed |
Most agents never need to go beyond Level 2.
Domain Examples
Business: CRM queries, email, calendar, approvals
Research: Database search, document analysis, citations
Operations: Monitoring, tickets, notifications, escalation
Creative: Asset generation, editing, collaboration, review
The pattern is universal. Only the capabilities change.
Key Principles
- The model IS the agent - Code just runs the loop
- Capabilities enable - What it CAN do
- Knowledge informs - What it KNOWS how to do
- Constraints focus - Limits create clarity
- Trust liberates - Let the model reason
- Iteration reveals - Start minimal, evolve from usage
Anti-Patterns
| Pattern | Problem | Solution |
|---|
| Over-engineering | Complexity before need | Start simple |
| Too many capabilities | Model confusion | 3-5 to start |
| Rigid workflows | Can't adapt | Let model decide |
| Front-loaded knowledge | Context bloat | Load on-demand |
| Micromanagement | Undercuts intelligence | Trust the model |
Resource Routing
Load only what the current task needs:
| User need | Resource | Do not load when |
|---|
| Understand or debate the architecture | references/agent-philosophy.md | The user only needs runnable starter code |
| Inspect the smallest complete loop | references/minimal-agent.py | The task is conceptual or provider-neutral |
| Add or adapt individual capabilities | references/tool-templates.py | No implementation is requested |
| Design context-isolated child agents | references/subagent-pattern.py | A single-agent loop is sufficient |
| Generate a local Python starter project | scripts/init_agent.py | The user asked only for design advice |
The Python resources are learning-oriented examples, not a security boundary. Their shell capability requires user approval by default; production systems still need isolation, least privilege, audit logs, and policy appropriate to their threat model.
The Agent Mindset
From: "How do I make the system do X?"
To: "How do I enable the model to do X?"
From: "What's the workflow for this task?"
To: "What capabilities would help accomplish this?"
The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.
Give the model capabilities and knowledge. Trust it to figure out the rest.