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{ali_brain_methods}
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## Agent Isolation & Placeholders
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Each agent runs in isolation - it has no knowledge of what agents came before or after it, or what they produced. Placeholders are the mechanism for passing context between agents.
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Without placeholders, a multi-agent workflow is just disconnected agents that can't build on each other's work.
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### Input & Output
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Every agent should define:
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-**Input**: How the agent receives context
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-**Output**: What the agent produces (with exact filename so it can become a placeholder)
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| Input Type | Use Case |
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|------------|----------|
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| Placeholder | Receiving output from previous agent (`{planner_output}`) |
| User Q&A | Interactive agent - input comes from user answers |
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| Specification | First agent receiving initial spec (`{specification}`) |
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| Output Type | Description |
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|-------------|-------------|
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| Named Output | Specify exact filename: `{agent-id}-output.md` - becomes placeholder for next agent |
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| No Output | Only for Q&A/interactive agents that collect info but don't produce artifacts |
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### How It Works
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```
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Agent A runs → writes to `planner-output.md` → registered as {planner_output}
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Agent B runs → receives {planner_output} → builds on Agent A's work
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```
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Step 04 collects input/output definitions. Step 05 validates placeholders are properly configured before generating the workflow.
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## Key Rules
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1.**Never skip steps** - Each step builds on previous (5 steps total), prompts are injected sequentially
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7.**Append to plan file immediately** - When user confirms, append data to plan file right away
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8.**Use TodoWrite** - Track progress through steps with the todo list
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9.**Guide user to correct step** - You don't have full context until you reach the right step. If user asks about something that belongs to a later step, guide them to proceed step-by-step. Process selected conditions in order. Example: if user selected `agents` + `prompts` and asks about prompts, say "Before we talk about prompts, let's handle agents first since it's earlier in your journey. Press Enter to proceed to agents."
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10.**Define input/output for agents** - Each agent needs to know where its context comes from (input) and what it produces (output). This enables placeholder chaining between agents.
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