Agent Builder
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
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
- 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.
- 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.
- 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:
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Purpose: What should this agent accomplish?
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Domain: What world does it operate in? (customer service, research, operations, creative...)
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Capabilities: What 3-5 actions are essential?
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Knowledge: What expertise does it need access to?
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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
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The model IS the agent - Code just runs the loop
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Capabilities enable - What it CAN do
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Knowledge informs - What it KNOWS how to do
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Constraints focus - Limits create clarity
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Trust liberates - Let the model reason
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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
Resources
Philosophy & Theory:
- references/agent-philosophy.md
- Deep dive into why agents work
Implementation:
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references/minimal-agent.py
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Complete working agent (~80 lines)
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references/tool-templates.py
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Capability definitions
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references/subagent-pattern.py
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Context isolation
Scaffolding:
- scripts/init_agent.py
- Generate new agent projects
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.