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  • Getting Started
    • Introduction
    • Why Rightbrain?
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    • Your First Task (5 min)
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  • Integration Patterns
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On this page
  • Why Tasks Work for Agent Enhancement
  • Agent + Task Patterns
  • Information Gathering & Analysis
  • Issue Resolution with Actions
  • Code-Aware Helper
  • Multi-Step Content Generation
  • Next Steps
Integration Patterns

Agent Enhancement with Tasks

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AI agents excel at reasoning and conversation but lack the tools to access your data, interact with your systems, or execute specialised operations.

Rightbrain Tasks become the agent’s toolkit - reliable, specialised capabilities the agent can invoke when needed.

The agent decides which tool to use and when, Tasks ensure consistent execution.

Why Tasks Work for Agent Enhancement

Tasks give agents capabilities they can’t have natively:

  • Reliable Tools: Agents can rely on Tasks returning consistent, structured data. No parsing surprises or format variations - just dependable tools the agent can reason with.
  • Any Call Order: The agent determines which Tasks to call and in what order, based on conversation context. Each Task works independently without needing coordination.
  • Reuse Across Use Cases: Tasks built for internal tools or workflows can also be used by agents. The same invoice extractor can power an expense workflow, Slack bot, and agent toolkit alike.
  • Your Context: Tasks understand your terminology, follow your business rules, and connect to your systems. Agents gain organisation-specific capabilities, not generic tools.

Agent + Task Patterns

Research Assistant
Customer Service Agent
Development Assistant
Content Creation Assistant

Information Gathering & Analysis

The Scenario: Executive asks “What’s happening with our enterprise customers this quarter?”

Agent Reasoning Process:

  1. Identifies needed information: customer data, revenue trends, support patterns
  2. Determines which Tasks to call
  3. Synthesises results into insights

Tasks Used:

1

Customer Segment Query

Agent calls: Customer data Task
Input: Segment=“enterprise”, timeframe=“Q1 2025”
Output: List of enterprise customers with ARR, growth, health scores
Agent uses: Identifies high-value and at-risk accounts

2

Revenue Analysis

Agent calls: Revenue trend Task
Input: Customer IDs from previous Task
Output: Revenue by customer, MoM growth, forecast
Agent uses: Spots patterns in growth/decline

3

Support Pattern Check

Agent calls: Support ticket analyser Task
Input: Same customer IDs
Output: Ticket volume, sentiment, top issues
Agent uses: Correlates support issues with revenue trends

4

Synthesis

Agent reasoning: Combines all data, identifies:

  • 3 customers showing churn risk (declining usage + negative support sentiment)
  • 5 customers expanding (increased usage + feature requests)
  • 2 customers perfect for case studies (high satisfaction + ROI achieved)

Agent response: Structured summary with specific recommendations

Why This Works:

  • Agent decides which Tasks to call based on question
  • Tasks provide reliable, structured data
  • Agent synthesises insights across multiple data sources
  • Composable: Same Tasks work in dashboards, workflows, and this agent

Ready to build and integrate Tasks into your existing agent? 

Next Steps

MCP Setup Guide

Configure Model Context Protocol integration

API Reference

Function calling integration details

The best agent enhancements start with 3-5 focused Tasks that address clear needs. Agent orchestration + reliable Tasks = powerful combination.