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Agents are autonomous AI assistants that use language models, tools, memory, and reasoning to accomplish tasks.

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Agents vs Gateways

AspectAgentGateway
PurposeAutonomous task executionTool exposure
Reasoning✓ Complex logic, planning✗ Pass-through only
Memory✓ Conversation history✗ Stateless
Autonomy✓ Makes decisions✗ User-driven
Channels✓ Telegram, Slack, SMS✗ MCP protocol only
Model✓ Own model selection✓ Configurable
Use Agents when: You want autonomous, intelligent behavior with memory and decision-making. Use Gateways when: You want to expose tools to MCP clients without autonomous execution.

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Key Features

  • System Prompts - Define agent personality and behavior
  • Tool Access - Use capabilities, connectors, plugins
  • Memory - Remember conversation context
  • Model Routing - Smart or specific model selection
  • Channels - Telegram, Slack, SMS integration
  • Automation - Scheduled and event-triggered runs

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Creating an Agent

  1. Agents > Create Agent
  2. Enter name and description
  3. Set system prompt (optional starter provided)
  4. Choose model routing
  5. Attach capabilities
  6. Click Create
Agent immediately ready to use.

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Using Agents

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Direct Chat

In Console, chat directly with agent. Test and iterate.

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Channels

Deploy to messaging platforms:
  • Telegram
  • Slack
  • SMS
Users interact via their preferred platform.

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API

Programmatic execution: 
curl -X POST https://api.noorle.com/v1/agents/{id}/run \
  -H "Authorization: Bearer {token}" \
  -d '{"prompt": "What is 2+2?"}'

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Automation

Scheduled or event-triggered execution via Automations.

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Agent Capabilities

Creating an Agent

Step-by-step guide to create agents.

System Prompts

Write effective prompts to guide agent behavior.

Model Routing

Choose models (Smart vs Specific).

Attaching Capabilities

Add tools to agents.

Sub-Agents

Multi-agent delegation and handoffs.

Memory & Context

Conversation memory configuration.

Omni Tool

LLM-based tool discovery.

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Common Agent Patterns

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Research Agent

  • Web Search
  • Browser (visual scraping)
  • Knowledge Retrieval
  • Code Runner (analyze data)

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Customer Support

  • Knowledge Retrieval (FAQ, docs)
  • HTTP Client (lookup customer data)
  • Slack channel
  • Warm handoff to human

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Data Analysis

  • Files (read/write)
  • Code Runner (Python)
  • Knowledge Retrieval (context)
  • HTTP Client (fetch data)

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Content Creator

  • Web Search (research)
  • Browser (gather examples)
  • Code Runner (process data)
  • Slack integration

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Agent State

Agents have lifecycle states:
  • Active - Enabled and running
  • Inactive - Disabled, not running
  • Archived - Removed from view
Toggle Active in agent settings to enable/disable.

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Monitoring

Track agent performance:
  1. Agents > Select agent
  2. Analytics tab shows:
    • Response count
    • Average latency
    • Token usage
    • Error rate
  3. Activity tab shows recent interactions

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Best Practices

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System Prompts

  • Be specific and clear
  • Set expected tone and behavior
  • Include guardrails
  • Explain constraints

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Tool Selection

  • Attach only needed tools
  • More tools = higher latency
  • Organize by use case
  • Document each tool

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Memory

  • Configure appropriate working memory
  • Set summary thresholds
  • Monitor context window
  • Archive old conversations

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Channels

  • Set appropriate platform
  • Configure authorization
  • Test before deployment
  • Monitor for errors

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Cost

For current pricing details, see Pricing. Monitor in Account > Usage dashboard.

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Next Steps