Update Autonomous Agent Design Document to Replace Schedule with Clock Trigger
- Changed terminology from "Schedule" to "Clock" throughout the document to reflect the new trigger type. - Revised flowcharts and diagrams to incorporate the clock context, clarifying the agent's operational phases based on time. - Updated configuration examples and descriptions to align with the new clock trigger settings, enhancing clarity on how agents operate under different timing modes. - Improved documentation for the Inspiration phase to specify its reliance on clock context, ensuring better understanding of the agent's decision-making process.
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1 changed files with 539 additions and 49 deletions
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@ -21,7 +21,7 @@ An **Autonomous Agent** is an AI team member. It works on its own, makes decisio
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```mermaid
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flowchart TB
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subgraph Triggers["Triggers"]
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WC[/"⏰ Schedule"/]
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WC[/"⏰ Clock"/]
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HI[/"👤 Human"/]
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EV[/"📡 Event"/]
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end
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@ -64,7 +64,7 @@ flowchart TB
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Dedup -->|Dup| Cache
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Queue --> W1 & W2 & W3
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W1 & W2 & W3 --> TT
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TT -->|Schedule| P0
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TT -->|Clock| P0
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TT -->|Human/Event| P1
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P0 --> P1 --> P2 --> P3 --> P4 --> P5
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P5 --> KB & DB & Job
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@ -138,8 +138,8 @@ sequenceDiagram
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W->>E: Run
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alt Schedule trigger
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E->>A: P0: Inspiration
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alt Clock trigger
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E->>A: P0: Inspiration (with clock context)
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A-->>E: Report
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end
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@ -154,17 +154,17 @@ sequenceDiagram
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### 3.2 Triggers
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| Type | What | Config |
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| ------------ | ------------------------ | -------------------- |
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| **Schedule** | Timer (cron or interval) | `triggers.schedule` |
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| **Human** | Manual action | `triggers.intervene` |
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| **Event** | Webhook, DB change | `triggers.event` |
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| Type | What | Config |
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| --------- | ----------------------------- | -------------------- |
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| **Clock** | Timer (times/interval/daemon) | `triggers.clock` |
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| **Human** | Manual action | `triggers.intervene` |
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| **Event** | Webhook, DB change | `triggers.event` |
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All on by default. Turn off per agent:
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```yaml
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triggers:
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schedule: { enabled: true }
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clock: { enabled: true }
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intervene: { enabled: true, actions: ["add_task", "pause"] }
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event: { enabled: false }
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```
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@ -219,27 +219,28 @@ type AgentCache struct {
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### 4.1 Overview
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```
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Schedule: P0 → P1 → P2 → P3 → P4 → P5
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Clock: P0 → P1 → P2 → P3 → P4 → P5
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Human/Event: P1 → P2 → P3 → P4 → P5
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```
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| Phase | Agent | In | Out | When |
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| ----- | ----------- | ---------------- | --------------- | ------------- |
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| P0 | Inspiration | Data, news, time | Report | Schedule only |
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| P1 | Goal Gen | Report + history | Goals | Always |
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| P2 | Task Plan | Goals + tools | Tasks | Always |
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| P3 | Validator | Results | Checked results | Always |
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| P4 | Delivery | All results | Email/File | Always |
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| P5 | Learning | Summary | KB entries | Always |
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| Phase | Agent | In | Out | When |
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| ----- | ----------- | ------------------- | --------------- | ---------- |
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| P0 | Inspiration | Clock + Data + News | Report | Clock only |
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| P1 | Goal Gen | Report + history | Goals | Always |
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| P2 | Task Plan | Goals + tools | Tasks | Always |
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| P3 | Validator | Results | Checked results | Always |
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| P4 | Delivery | All results | Email/File | Always |
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| P5 | Learning | Summary | KB entries | Always |
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### 4.2 P0: Inspiration (Schedule only)
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### 4.2 P0: Inspiration (Clock only)
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**Skipped for Human/Event triggers.** They already have clear intent.
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Gathers info to help make good goals:
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Gathers info to help make good goals. **Clock context is key input** - Agent knows what time it is and can decide what to do (e.g., 5pm Friday → write weekly report).
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```go
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type InspirationReport struct {
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Clock ClockContext // Current time context
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Summary string // What's happening
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Highlights []Highlight // Key changes
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Opportunities []Opportunity // Chances to act
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@ -247,17 +248,29 @@ type InspirationReport struct {
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WorldInsights []WorldInsight // News from outside
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Suggestions []string // What to focus on
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}
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type ClockContext struct {
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Now time.Time // Current time
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Hour int // 0-23
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DayOfWeek string // Monday, Tuesday...
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DayOfMonth int // 1-31
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IsWeekend bool
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IsMonthStart bool // 1st-3rd
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IsMonthEnd bool // last 3 days
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IsQuarterEnd bool
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// Agent uses this to decide: "It's 5pm Friday, time for weekly report"
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}
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```
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**Sources:**
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- **Clock**: Current time, day of week, month end, etc.
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- Internal: Data changes, events, feedback, pending work
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- External: Web search (news, competitors)
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- Time: Day of week, deadlines
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### 4.3 P1: Goals
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**For Schedule:** Uses inspiration report to make goals.
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**For Clock:** Uses inspiration report (with clock context) to make goals. Agent decides based on time what's important now.
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**For Human/Event:** Uses the input directly as goals (or to generate goals).
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@ -331,7 +344,7 @@ Save to KB:
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```go
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type Config struct {
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Triggers *Triggers `json:"triggers,omitempty"`
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Schedule *Schedule `json:"schedule,omitempty"`
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Clock *Clock `json:"clock,omitempty"`
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Identity *Identity `json:"identity"`
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Quota *Quota `json:"quota"`
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PrivateKB *KB `json:"private_kb"`
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@ -349,7 +362,7 @@ type Config struct {
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```go
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// Triggers - all on by default
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type Triggers struct {
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Schedule *Trigger `json:"schedule,omitempty"`
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Clock *Trigger `json:"clock,omitempty"`
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Intervene *Trigger `json:"intervene,omitempty"`
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Event *Trigger `json:"event,omitempty"`
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}
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@ -359,14 +372,21 @@ type Trigger struct {
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Actions []string `json:"actions,omitempty"` // for intervene
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}
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// Schedule
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type Schedule struct {
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Type string `json:"type"` // cron | interval
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Expr string `json:"expr"` // "0 9 * * 1-5" or "1h"
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TZ string `json:"tz"`
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Timeout string `json:"timeout"`
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// Clock - when to wake up
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type Clock struct {
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Mode string `json:"mode"` // "times" | "interval" | "daemon"
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Times []string `json:"times"` // for mode=times: ["09:00", "14:00", "17:00"]
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Days []string `json:"days"` // ["Mon", "Tue", "Wed", "Thu", "Fri"] or ["*"]
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Every string `json:"every"` // for mode=interval: "30m", "1h"
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TZ string `json:"tz"` // Asia/Shanghai
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Timeout string `json:"timeout"` // max run time per execution
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}
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// Clock modes:
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// - times: Run at specific times (e.g., 9am, 2pm, 5pm)
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// - interval: Run every X duration (e.g., every 30 minutes)
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// - daemon: Run continuously (e.g., monitoring, data sync)
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// Identity
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type Identity struct {
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Role string `json:"role"`
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@ -396,7 +416,7 @@ type Learn struct {
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// Resources
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type Resources struct {
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P0 string `json:"p0"` // Inspiration (Schedule only)
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P0 string `json:"p0"` // Inspiration (Clock only)
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P1 string `json:"p1"` // Goals
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P2 string `json:"p2"` // Tasks
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P3 string `json:"p3"` // Validation
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@ -446,13 +466,14 @@ type Action struct {
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"agent_id": "sales-bot",
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"agent_config": {
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"triggers": {
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"schedule": { "enabled": true },
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"clock": { "enabled": true },
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"intervene": { "enabled": true },
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"event": { "enabled": false }
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},
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"schedule": {
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"type": "cron",
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"expr": "0 9 * * 1-5",
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"clock": {
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"mode": "times",
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"times": ["09:00", "14:00", "17:00"],
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"days": ["Mon", "Tue", "Wed", "Thu", "Fri"],
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"tz": "Asia/Shanghai",
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"timeout": "30m"
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},
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@ -549,7 +570,7 @@ flowchart LR
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P5[P5: Learn]
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end
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T -->|Schedule| P0
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T -->|Clock| P0
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T -->|Human/Event| P1
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P0 --> P1
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P1 --> P2 --> P3 --> P4 --> P5
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@ -558,7 +579,7 @@ flowchart LR
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```mermaid
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stateDiagram-v2
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[*] --> Triggered
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Triggered --> P0_Inspiration: Schedule
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Triggered --> P0_Inspiration: Clock
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Triggered --> P1_Goals: Human/Event
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P0_Inspiration --> P1_Goals
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P1_Goals --> P2_Tasks
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@ -624,7 +645,7 @@ Made on agent create: `agent_{team_id}_{agent_id}_kb`
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**Types:**
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- `schedule`: Timer
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- `clock`: Timer (with time context)
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- `intervene`: Human action
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- `event`: Webhook, DB change
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- `callback`: Async result
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@ -670,7 +691,7 @@ type State struct {
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StartTime time.Time
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EndTime *time.Time
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Status Status // pending | running | completed | failed
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Phase Phase // inspiration (schedule only) | goal_gen | task_plan | run | deliver | learn
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Phase Phase // inspiration (clock only) | goal_gen | task_plan | run | deliver | learn
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Goals []Goal
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Tasks []Task
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Error string
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@ -717,16 +738,40 @@ CREATE TABLE autonomous_executions (
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```yaml
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triggers:
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schedule: { enabled: true }
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clock: { enabled: true }
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intervene: { enabled: true, actions: [...] }
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event: { enabled: false }
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```
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### Clock
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```yaml
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# Mode 1: Specific times
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clock:
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mode: times
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times: ["09:00", "14:00", "17:00"]
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days: ["Mon", "Tue", "Wed", "Thu", "Fri"]
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tz: Asia/Shanghai
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timeout: 30m
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# Mode 2: Interval
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clock:
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mode: interval
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every: 30m # run every 30 minutes
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timeout: 10m
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# Mode 3: Daemon (continuous monitoring/sync)
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clock:
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mode: daemon # restart immediately after each run
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timeout: 5m # max time per run
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# Use case: Data sync agent, system monitor agent
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```
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### Phase Agents
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```yaml
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resources:
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p0: "__yao.inspiration" # Schedule only
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p0: "__yao.inspiration" # Clock only
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p1: "__yao.goals"
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p2: "__yao.tasks"
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p3: "__yao.validation"
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@ -743,12 +788,457 @@ quota:
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priority: 5 # 1-10
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```
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### Schedule
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---
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```yaml
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schedule:
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type: cron
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expr: "0 9 * * 1-5"
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tz: Asia/Shanghai
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timeout: 30m
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## 11. Examples
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Each example shows a different trigger mode:
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| Example | Trigger | Mode | Scenario |
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| ------- | ------- | --------- | -------------------------------------------- |
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| 11.1 | Clock | times | SEO/GEO Content - daily content optimization |
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| 11.2 | Clock | interval | Competitor Monitor - check every 2 hours |
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| 11.3 | Clock | daemon | Research Analyst - continuous insight mining |
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| 11.4 | Human | intervene | Sales Assistant - manager assigns tasks |
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| 11.5 | Event | event | Expense Processor - process new submissions |
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---
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### 11.1 SEO/GEO Content Agent (Clock: times)
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**Trigger:** Clock - specific times daily
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**Role:** AI Marketing - auto-generate and optimize SEO/GEO content.
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```json
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{
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"agent_id": "seo-content",
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"agent_config": {
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"triggers": {
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"clock": { "enabled": true },
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"intervene": { "enabled": true }
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},
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"clock": {
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"mode": "times",
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"times": ["06:00", "18:00"],
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"days": ["Mon", "Tue", "Wed", "Thu", "Fri"],
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"tz": "Asia/Shanghai"
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},
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"identity": {
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"role": "SEO/GEO Content Specialist",
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"duties": [
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"Research trending keywords in our industry",
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"Generate SEO-optimized articles (2-3 per day)",
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"Optimize existing content for GEO (AI search)",
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"Track keyword rankings and adjust strategy",
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"A/B test titles and meta descriptions"
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]
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},
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"resources": {
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"agents": ["keyword-researcher", "content-writer", "seo-optimizer"],
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"mcp": [
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{ "id": "google-search", "tools": ["trends", "rankings"] },
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{ "id": "cms", "tools": ["create", "update", "publish"] }
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]
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},
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"delivery": {
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"type": "notify",
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"opts": { "channel": "marketing-team" }
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}
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}
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}
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```
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**Example run at 06:00 Monday:**
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```
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P0 Inspiration:
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Clock: Monday 06:00, start of week
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Data:
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- Keyword "AI应用开发" trending (+45% this week)
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- Our article ranks #8, competitor #2
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- 3 articles need GEO optimization
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World: New AI regulation announced last Friday
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P1 Goals:
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1. Write new article targeting "AI应用开发"
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2. Optimize 3 old articles for GEO
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3. Update meta descriptions for top 5 pages
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P2 Tasks:
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1. Research "AI应用开发" keywords → keyword-researcher
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2. Write article with SEO structure → content-writer
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3. Add FAQ schema for GEO → seo-optimizer
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4. Publish to CMS → cms.publish
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P4 Delivery:
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→ Notify: "Published: 'AI应用开发完整指南' - targeting 12 keywords"
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P5 Learn:
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- "AI应用开发" articles perform well on Monday morning
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- FAQ schema improves GEO visibility by 30%
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```
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---
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### 11.2 Competitor Monitor (Clock: interval)
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**Trigger:** Clock - every 2 hours
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**Role:** Monitor competitors, track market changes, alert on important updates.
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```json
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{
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"agent_id": "competitor-monitor",
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"agent_config": {
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"triggers": {
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"clock": { "enabled": true }
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},
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"clock": {
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"mode": "interval",
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"every": "2h"
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},
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"identity": {
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"role": "Competitor Intelligence Analyst",
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"duties": [
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"Monitor competitor websites for changes",
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"Track competitor pricing updates",
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"Watch for new product launches",
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"Analyze competitor content strategy",
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"Alert team on significant changes"
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]
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},
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"resources": {
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"agents": ["web-scraper", "diff-analyzer", "report-writer"],
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"mcp": [{ "id": "web-search", "tools": ["search", "news"] }]
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},
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"delivery": {
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"type": "webhook",
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"opts": { "url": "https://slack.com/webhook/competitor-alerts" }
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}
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}
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}
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```
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**Example run detecting competitor change:**
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```
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P0 Inspiration:
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Clock: Tuesday 14:00
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Data:
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- Competitor A: pricing page changed
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- Competitor B: new blog post about "AI agents"
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- Competitor C: no changes
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P1 Goals:
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1. Analyze Competitor A pricing change
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2. Summarize Competitor B's new content
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3. Assess impact on our positioning
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P2 Tasks:
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1. Scrape old vs new pricing → web-scraper
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2. Compare pricing tiers → diff-analyzer
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3. Generate competitive analysis → report-writer
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P3 Execute:
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- Competitor A: dropped price 20% on enterprise tier
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- Competitor B: targeting same keywords as us
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P4 Delivery:
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→ Slack: "🚨 Competitor A cut enterprise price 20% - review needed"
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P5 Learn:
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- Competitor A tends to change pricing on Tuesdays
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- Price changes often precede feature launches
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```
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---
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### 11.3 Industry Research Analyst (Clock: daemon)
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**Trigger:** Clock - continuous daemon mode
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**Role:** Continuously read industry news, papers, social media; extract insights; build knowledge.
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```json
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{
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"agent_id": "research-analyst",
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"agent_config": {
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"triggers": {
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"clock": { "enabled": true }
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},
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"clock": {
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"mode": "daemon",
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"timeout": "10m"
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},
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"identity": {
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"role": "Industry Research Analyst",
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"duties": [
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"Continuously scan industry news and papers",
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"Analyze trends and extract key insights",
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"Identify emerging technologies and competitors",
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"Build and maintain industry knowledge base",
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"Alert team on significant developments"
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]
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},
|
||||
"resources": {
|
||||
"agents": ["content-reader", "insight-extractor", "report-writer"],
|
||||
"mcp": [
|
||||
{ "id": "web-search", "tools": ["search", "news"] },
|
||||
{ "id": "arxiv", "tools": ["search", "fetch"] },
|
||||
{ "id": "twitter", "tools": ["search", "trends"] }
|
||||
]
|
||||
},
|
||||
"delivery": {
|
||||
"type": "notify",
|
||||
"opts": { "channel": "research-insights" }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Example continuous run:**
|
||||
|
||||
```
|
||||
Run #1 (09:00):
|
||||
P0: Scan sources
|
||||
- 15 new AI news articles
|
||||
- 3 new papers on arXiv
|
||||
- Twitter: "AI Agent" trending
|
||||
P1: Goals:
|
||||
1. Read and analyze new content
|
||||
2. Extract insights relevant to our business
|
||||
3. Update knowledge base
|
||||
P2: Tasks:
|
||||
1. Read articles → content-reader
|
||||
2. Analyze papers → content-reader
|
||||
3. Extract insights → insight-extractor
|
||||
P3: Execute:
|
||||
- Article: "OpenAI releases new agent framework"
|
||||
Insight: Validates our direction, watch for API changes
|
||||
- Paper: "Multi-agent collaboration patterns"
|
||||
Insight: Useful for our agent design, save to KB
|
||||
- Twitter: Sentiment positive on AI agents
|
||||
P4: Notify: "📚 3 new insights added to KB"
|
||||
P5: Learn: OpenAI news = high relevance, prioritize
|
||||
→ Restart immediately
|
||||
|
||||
Run #2 (09:12):
|
||||
P0: Scan sources
|
||||
- 2 new articles (low relevance)
|
||||
- No new papers
|
||||
- Twitter: Normal activity
|
||||
P1: Low-value content, skip deep analysis
|
||||
P5: Learn: Mid-morning usually quiet
|
||||
→ Restart immediately
|
||||
|
||||
Run #3 (09:25):
|
||||
P0: Scan sources
|
||||
- Breaking: "Competitor X raises $100M for AI platform"
|
||||
P1: Goals:
|
||||
1. Deep analyze competitor news
|
||||
2. Assess impact on our market
|
||||
3. Alert team immediately
|
||||
P2: Tasks:
|
||||
1. Gather all competitor X info → web-search
|
||||
2. Analyze their positioning → insight-extractor
|
||||
3. Write competitive brief → report-writer
|
||||
P3: Execute:
|
||||
- Competitor X: Focus on enterprise, similar target market
|
||||
- Funding: Will likely expand sales team
|
||||
- Threat level: Medium-High
|
||||
P4: Notify: "🚨 Competitor X raised $100M - brief attached"
|
||||
P5: Learn: Funding news = always high priority
|
||||
→ Restart immediately
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 11.4 Sales Assistant (Human: intervene)
|
||||
|
||||
**Trigger:** Human intervention - sales manager assigns tasks
|
||||
|
||||
**Role:** Help sales team with research, proposals, follow-ups when manager assigns work.
|
||||
|
||||
```json
|
||||
{
|
||||
"agent_id": "sales-assistant",
|
||||
"agent_config": {
|
||||
"triggers": {
|
||||
"clock": { "enabled": false },
|
||||
"intervene": {
|
||||
"enabled": true,
|
||||
"actions": ["add_task", "adjust_goal", "pause"]
|
||||
}
|
||||
},
|
||||
"identity": {
|
||||
"role": "Sales Assistant",
|
||||
"duties": [
|
||||
"Research assigned prospects and companies",
|
||||
"Prepare customized proposals and presentations",
|
||||
"Draft follow-up emails",
|
||||
"Analyze deal history and suggest strategies",
|
||||
"Prepare meeting briefs"
|
||||
]
|
||||
},
|
||||
"resources": {
|
||||
"agents": ["company-researcher", "proposal-writer", "email-drafter"],
|
||||
"mcp": [
|
||||
{ "id": "crm", "tools": ["query", "update"] },
|
||||
{ "id": "linkedin", "tools": ["search", "profile"] },
|
||||
{ "id": "email", "tools": ["draft", "send"] }
|
||||
]
|
||||
},
|
||||
"delivery": {
|
||||
"type": "email",
|
||||
"opts": { "to": ["sales-manager@company.com"] }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Example: Sales manager assigns task:**
|
||||
|
||||
```
|
||||
Sales Manager Input:
|
||||
Action: add_task
|
||||
Description: "Meeting with BigCorp CTO tomorrow. Prepare materials.
|
||||
They do smart manufacturing, $150M revenue, digital transformation."
|
||||
|
||||
Agent Execution (no P0 for human trigger):
|
||||
P1 Goals (from human input):
|
||||
1. Research BigCorp and their CTO
|
||||
2. Prepare meeting brief
|
||||
3. Draft customized proposal
|
||||
|
||||
P2 Tasks:
|
||||
1. Research BigCorp → company-researcher
|
||||
- Company background, recent news
|
||||
- Digital transformation status
|
||||
- Potential pain points
|
||||
2. Research CTO profile → linkedin.profile
|
||||
- Background, interests
|
||||
- Recent posts/articles
|
||||
3. Prepare meeting brief → proposal-writer
|
||||
4. Draft proposal → proposal-writer
|
||||
|
||||
P3 Execute:
|
||||
- BigCorp: Leading smart manufacturing, 3 factories, implementing MES
|
||||
- CTO John: Ex-Google, focused on AI+Manufacturing, recent post on "AI QC"
|
||||
- Pain point: High QC labor cost, 2% defect miss rate
|
||||
- Opportunity: Our AI QC solution can reduce miss rate to 0.1%
|
||||
|
||||
P4 Delivery:
|
||||
→ Email to sales manager:
|
||||
- Attachment 1: BigCorp Research Report (PDF)
|
||||
- Attachment 2: CTO Profile Brief
|
||||
- Attachment 3: Custom Proposal - AI QC Solution
|
||||
- Attachment 4: Meeting Agenda Suggestion
|
||||
|
||||
Sales Manager Follow-up:
|
||||
Action: add_task
|
||||
Description: "Also prepare some similar case studies, manufacturing preferred"
|
||||
|
||||
Agent Continues:
|
||||
P1: Find similar manufacturing case studies
|
||||
P2: Search CRM for manufacturing wins
|
||||
P3: Found 3 cases: Auto parts factory, Electronics plant, Food processing
|
||||
P4: Email: "3 manufacturing case studies attached"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 11.5 Lead Processor (Event: webhook)
|
||||
|
||||
**Trigger:** Event - new lead from website/CRM
|
||||
|
||||
**Role:** Instantly process and qualify new leads, route to sales.
|
||||
|
||||
```json
|
||||
{
|
||||
"agent_id": "lead-processor",
|
||||
"agent_config": {
|
||||
"triggers": {
|
||||
"clock": { "enabled": false },
|
||||
"event": { "enabled": true }
|
||||
},
|
||||
"events": [
|
||||
{
|
||||
"type": "webhook",
|
||||
"source": "/webhook/leads",
|
||||
"filter": { "event_types": ["lead.created"] }
|
||||
},
|
||||
{
|
||||
"type": "database",
|
||||
"source": "crm_leads",
|
||||
"filter": { "trigger": "insert" }
|
||||
}
|
||||
],
|
||||
"identity": {
|
||||
"role": "Lead Qualification Specialist",
|
||||
"duties": [
|
||||
"Instantly process new leads",
|
||||
"Enrich lead data (company info, LinkedIn)",
|
||||
"Score lead quality (1-100)",
|
||||
"Route hot leads to sales immediately",
|
||||
"Add cold leads to nurture sequence"
|
||||
]
|
||||
},
|
||||
"resources": {
|
||||
"agents": ["data-enricher", "lead-scorer"],
|
||||
"mcp": [
|
||||
{ "id": "clearbit", "tools": ["enrich"] },
|
||||
{ "id": "crm", "tools": ["update", "assign"] },
|
||||
{ "id": "email", "tools": ["send"] }
|
||||
]
|
||||
},
|
||||
"delivery": {
|
||||
"type": "webhook",
|
||||
"opts": { "url": "https://slack.com/webhook/sales-leads" }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Example: New lead event:**
|
||||
|
||||
```
|
||||
Event Received:
|
||||
Type: lead.created
|
||||
Data: {
|
||||
name: "John Smith",
|
||||
email: "john@bigcorp.com",
|
||||
company: "BigCorp",
|
||||
message: "Interested in Enterprise pricing, team of 50"
|
||||
}
|
||||
|
||||
Agent Execution (no P0 for events):
|
||||
P1 Goals:
|
||||
1. Enrich lead data
|
||||
2. Score lead quality
|
||||
3. Route appropriately
|
||||
|
||||
P2 Tasks:
|
||||
1. Lookup company info → clearbit.enrich
|
||||
2. Calculate lead score → lead-scorer
|
||||
3. Update CRM → crm.update
|
||||
4. Notify sales → slack webhook
|
||||
|
||||
P3 Execute:
|
||||
- Company: BigCorp, 500 employees, Series C
|
||||
- LinkedIn: VP of Engineering
|
||||
- Lead Score: 85/100 (HOT)
|
||||
- Reason: Enterprise inquiry, decision maker, funded company
|
||||
|
||||
P4 Delivery:
|
||||
→ Slack: "🔥 HOT LEAD (85/100): John Smith @ BigCorp
|
||||
- 500 employees, Series C
|
||||
- Interested in Enterprise (50 seats)
|
||||
- Assigned to: Sales Rep A"
|
||||
→ CRM: Lead updated, assigned to Sales Rep A
|
||||
→ Email to lead: "Thanks for your inquiry. Our sales rep will contact you within 1 hour."
|
||||
|
||||
P5 Learn:
|
||||
- BigCorp profile saved for future reference
|
||||
- VP-level leads from funded companies = high conversion
|
||||
```
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue