Understanding Agents
Core concepts of SwiftClaw agents
Understanding Agents
An agent is an autonomous AI program that processes requests and takes actions.
What is an Agent?
An agent consists of:
- Model: AI model (GPT-4, Claude, etc.)
- Memory: Context storage
- Tools: Functions for external interactions
- Configuration: Behavior parameters
Agent Lifecycle
1. Creation
swiftclaw agents create customer-support \
--model gpt-4 \
--memory persistent2. Configuration
{
"model": "gpt-4",
"temperature": 0.7,
"max_tokens": 2000,
"memory": {
"type": "persistent",
"ttl": "30d"
}
}3. Deployment
swiftclaw deploy customer-support --env production4. Invocation
curl https://api.swiftclaw.io/agents/customer-support/invoke \
-H "Authorization: Bearer sk_live_abc123..." \
-d '{"message": "Hello"}'Agent Types
Conversational Agents
Handle chat interactions:
agent = Agent(
name="chatbot",
model="gpt-4",
system_prompt="You are a helpful assistant."
)Task Agents
Execute specific tasks:
agent = Agent(
name="data-processor",
model="gpt-4",
tools=["fetch_data", "transform_data"]
)Multi-Agent Systems
Coordinate multiple agents:
coordinator = Agent(
name="coordinator",
agents=["researcher", "writer", "reviewer"]
)Agent Configuration
Model Selection
{
"model": {
"primary": "gpt-4",
"fallbacks": ["claude-3-sonnet"]
}
}Memory Settings
{
"memory": {
"type": "persistent",
"scope": "per-user",
"ttl": "30d"
}
}Tool Configuration
{
"tools": [
"search_knowledge_base",
"create_ticket",
"send_email"
]
}Agent Isolation
Each agent runs in isolation:
- Dedicated resources
- Independent workspaces
- Separate memory
- Individual scaling
Security: Agents cannot access each other's data unless explicitly configured.
Next Steps
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