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Formato Specifiche Agenti

Gli agenti Tajo sono definiti in file markdown. Ogni file contiene frontmatter YAML (identità, strumenti, vincoli) e un corpo markdown (istruzioni, strategia, regole). Questo formato si ispira ai pattern di agenti in produzione usati nei sistemi di orchestrazione multi-agente.

Struttura del File

---
name: agent-name
description: What this agent does (max 160 chars)
version: 1.0.0
temperature: 0.2
max_tokens: 4096
tools:
- brevo_contacts
- brevo_email_campaign_management
- brevo_sms_campaigns
triggers:
- event: cart_abandoned
- schedule: "0 */4 * * *"
permissions:
- contacts:read
- email:send
- sms:send
---
# Agent Name
Instructions for the agent in natural language...

Campi Frontmatter

Campi Obbligatori

CampoTipoDescrizione
namestringaIdentificatore univoco in kebab-case (es. cart-recovery-agent)
descriptionstringaCosa fa questo agente (max 160 caratteri)
versionstringaVersione semantica (es. 1.0.0)
toolsarrayModuli server MCP Brevo a cui questo agente può accedere

Campi Comportamentali

CampoTipoDefaultDescrizione
temperaturefloat0.3Temperatura LLM. Più bassa = più deterministica. Usa 0.1-0.2 per operazioni sui dati, 0.3-0.5 per la progettazione di campagne
max_tokensintero4096Lunghezza massima risposta per turno
modelstringaclaude-sonnet-4-6Modello LLM da usare

Campi Trigger

CampoTipoDefaultDescrizione
triggersarray[]Eventi, pianificazioni o webhook che attivano questo agente
triggers[].eventstringa-Nome evento (es. cart_abandoned, customer_created)
triggers[].schedulestringa-Espressione cron (es. 0 9 * * * per le 9 del mattino quotidiane)
triggers[].webhookstringa-Percorso webhook (es. /agents/cart-recovery/trigger)
triggers[].conditionsarray[]Condizioni filtro per il trigger
triggers[].debouncestringa-Finestra di debounce (es. 5m, 1h)

Campi Permessi

CampoTipoDefaultDescrizione
permissionsarray[]Scope di permesso richiesti per la traccia di audit
related_agentsarray[]ID agenti a cui questo agente può delegare
escalationstringa-Dove instradare quando l’agente è incerto (human, supervisor-agent)

Strumenti: Mappatura ai Server MCP Brevo

Il campo tools fa riferimento ai nomi dei moduli server MCP Brevo. Ogni modulo si mappa a un endpoint specifico su mcp.brevo.com:

tools:
# Contacts & Segmentation
- brevo_contacts # /v1/brevo_contacts/mcp
- brevo_lists # /v1/brevo_lists/mcp
- brevo_segments # /v1/brevo_segments/mcp
- brevo_attributes # /v1/brevo_attributes/mcp
# Campaigns & Messaging
- brevo_email_campaign_management # /v1/brevo_email_campaign_management/mcp
- brevo_templates # /v1/brevo_templates/mcp
- brevo_sms_campaigns # /v1/brevo_sms_campaigns/mcp
- brevo_whatsapp_campaigns # /v1/brevo_whatsapp_campaigns/mcp
# Analytics
- brevo_campaign_analytics # /v1/brevo_campaign_analytics/mcp
# Sales CRM
- brevo_deals # /v1/brevo_deals/mcp
- brevo_companies # /v1/brevo_companies/mcp
- brevo_tasks # /v1/brevo_tasks/mcp
- brevo_pipelines # /v1/brevo_pipelines/mcp
- brevo_notes # /v1/brevo_notes/mcp

Tip

Usa il set minimo di strumenti di cui il tuo agente ha bisogno. Meno strumenti = migliore ragionamento IA e risposte più veloci. Vedi Server MCP Brevo per tutti i moduli disponibili.

Trigger

Trigger per Evento

Attiva l’agente quando accade qualcosa nel tuo sistema:

triggers:
- event: cart_abandoned
conditions:
- cart_value: "> 50"
- items_count: ">= 1"
- time_since_activity: "> 30m"
debounce: 5m

Trigger per Pianificazione

Esegui l’agente su una pianificazione ricorrente:

triggers:
- schedule: "0 9 * * MON" # Every Monday at 9am
timezone: "America/New_York"
- schedule: "0 */4 * * *" # Every 4 hours
- schedule: "0 0 1 * *" # First day of each month

Trigger Webhook

Invoca l’agente tramite HTTP:

triggers:
- webhook: /agents/win-back/trigger
method: POST
authentication: api_key

Corpo Markdown: Istruzioni

Il corpo della specifica agente è in linguaggio naturale. Scrivilo come se stessi istruendo un marketer esperto:

Struttura

# Agent Name
Context paragraph, what this agent does and why.
## Strategy
Step-by-step approach the agent should follow.
## Decision Framework
Rules for making choices (e.g., which channel to use based on cart value).
## Rules
Hard constraints, things the agent must ALWAYS or NEVER do.
## Templates
References to Brevo template IDs, SMS copy, WhatsApp templates.
## Metrics
Events to track for measuring success.

Scrivere Istruzioni Efficaci

Sii specifico sulla strategia, non solo sugli obiettivi:

## Bad
Re-engage churned customers.
## Good
When a customer hasn't purchased in 90+ days:
1. Check their last 3 orders for product category preferences
2. Create a personalized discount based on AOV (10% if AOV > $100, 15% if < $100)
3. Send email with subject line referencing their preferred category
4. Wait 72 hours, if no open, send SMS with discount code
5. Wait 7 days, if no purchase, mark as deep-churn and stop sequence

Definisci i guardrail esplicitamente:

## Rules
- NEVER send more than 3 messages per sequence
- NEVER contact customers who unsubscribed
- ALWAYS check if the customer converted before sending the next step
- ALWAYS respect quiet hours (no SMS 9pm-9am local time)
- If unsure about a decision, escalate to human review

Catene Multi-Agente

Per workflow complessi, componi più agenti in una catena. Ogni agente gestisce una fase, passando il contesto al successivo:

chain.yaml
name: quarterly-retention-campaign
steps:
- agent: customer-intelligence
input: |
Analyze customer segments for Q2 retention campaign.
Goal: {task}
Identify:
1. At-risk customers (declining purchase frequency)
2. VIP customers (top 10% by LTV)
3. Win-back candidates (90+ days since last order)
- agent: campaign-designer
input: |
Design retention campaigns for these segments:
{previous}
Create differentiated approaches per segment:
- At-risk: gentle nudge with product recommendations
- VIP: exclusive early access or loyalty reward
- Win-back: aggressive discount with urgency
- agent: campaign-executor
input: |
Execute these campaigns via Brevo:
{previous}
Use appropriate channels per segment preference.
Set up A/B tests for subject lines.
Schedule sends for optimal times.
- agent: campaign-reporter
input: |
Generate the retention campaign launch report:
{previous}
Include: segments targeted, campaigns created,
expected reach, A/B test configurations.

Variabili di Catena

VariabileDescrizione
{task}L’obiettivo/richiesta originale
{previous}Output dal passo precedente
{step_N}Output dal passo N (indice 0)
{artifacts_dir}Directory per gli output dei file

Specifiche Agenti Preconfigurati

Campaign Orchestrator

---
name: campaign-orchestrator
description: Design and execute multi-channel campaigns from natural language prompts
version: 2.0.0
temperature: 0.3
tools:
- brevo_contacts
- brevo_segments
- brevo_email_campaign_management
- brevo_templates
- brevo_sms_campaigns
- brevo_whatsapp_campaigns
- brevo_campaign_analytics
triggers:
- webhook: /agents/campaign/trigger
method: POST
---
# Campaign Orchestrator
You are a multi-channel marketing campaign specialist.
Given a campaign brief, you design, build, and launch
campaigns across email, SMS, and WhatsApp via Brevo.
## Process
1. Parse the campaign brief (audience, message, goal, timeline)
2. Create or identify the target segment in Brevo
3. Select the best channel(s) based on audience preference data
4. Build campaign content using existing templates or creating new ones
5. Configure send schedule and A/B tests
6. Launch and report initial delivery metrics
## Channel Selection
- Email: default for all campaigns
- SMS: add for time-sensitive offers or cart recovery
- WhatsApp: add for conversational campaigns or high-value segments
## Rules
- ALWAYS preview campaigns before sending
- NEVER send to unsubscribed contacts
- ALWAYS set up tracking for campaign attribution
- Maximum 2 A/B test variants per campaign

Customer Intelligence Agent

---
name: customer-intelligence
description: Autonomous segmentation, RFM scoring, and churn prediction
version: 1.5.0
temperature: 0.2
tools:
- brevo_contacts
- brevo_segments
- brevo_attributes
- brevo_lists
- brevo_campaign_analytics
triggers:
- schedule: "0 6 * * MON"
timezone: "UTC"
---
# Customer Intelligence Agent
You analyze customer data in Brevo to generate actionable
segments and insights for marketing teams.
## Weekly Analysis
1. Pull contact activity data from campaign analytics
2. Calculate RFM scores (Recency, Frequency, Monetary)
3. Identify segment shifts (customers moving between tiers)
4. Flag churn risks (declining engagement over 4+ weeks)
5. Generate segment recommendations for upcoming campaigns
## Segment Definitions
- Champions: R=5, F=5, M=5, recent, frequent, high-value
- Loyal: R>=3, F>=4, M>=3, consistent buyers
- At Risk: R<=2, F>=3, M>=3, were loyal, now fading
- Hibernating: R=1, F>=2, M>=2, long gone, were once active
- New: first purchase in last 30 days
## Output
Produce a markdown report with:
- Segment sizes and week-over-week changes
- Top 10 at-risk customers by LTV
- Recommended actions per segment
- Suggested campaign themes for the week

Distribuzione

Esecuzione di un Agente in Modo Programmatico

import { TajoAgent } from "@tajo/agent-sdk";
const agent = new TajoAgent({
specPath: "./agents/cart-recovery-agent.md",
brevoToken: process.env.BREVO_MCP_TOKEN,
model: "claude-sonnet-4-6",
// Only connect the MCP servers listed in the agent's tools field
autoConnectServers: true,
});
const result = await agent.run(
"Recover abandoned carts over $50 from the last 4 hours"
);
console.log(result.summary);
console.log(result.toolCalls); // Full audit trail
console.log(result.metrics); // Events tracked

Esecuzione tramite Claude Code

Terminal window
# Point to your agent spec and let Claude execute it
claude "Run the agent defined in ./agents/cart-recovery-agent.md for today's abandoned carts"

Pianificazione con Cron

Terminal window
# Run the customer intelligence agent every Monday at 6am
0 6 * * MON claude --print "Run ./agents/customer-intelligence.md weekly analysis" >> /var/log/tajo-agents.log 2>&1

Passi Successivi

Richiedi l’accesso anticipato

Inserisci il tuo nome e un indirizzo email o un numero di telefono. Ti contatteremo con tutte le informazioni per accedere a Tajo.

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