Gateway Agent Defaults

Chat-optimised defaults for YAML agents behind the gateway

The gateway now ships in the praisonai-bot package. praisonai serve gateway still works exactly as documented here; for a standalone install see praisonai-bot Migration.

from praisonaiagents import Agent

agent = Agent(
    name="chat-assistant",
    instructions="You are a helpful assistant.",
    reflect_on_output=False,
)
agent.start("Reply as fast as possible.")

Gateway agents use chat-optimised defaults to ensure fast response times for interactive chat applications.

# gateway.yaml — fast by default (reflection=false)
agents:
  assistant:
    instructions: "You are a helpful AI assistant."
    model: gpt-4o-mini

The user chats with the assistant; gateway defaults keep replies fast by disabling reflection on the agent path.

Quick Start

1. Fast by default

   Create a minimal bot.yaml with fast defaults:

   agents:
     assistant:
       instructions: "You are a helpful AI assistant."
       model: gpt-4o-mini
   

   Response latency: ~1.6 seconds on short prompts.

2. Opt in to reflection

   Add reflection for higher quality responses:

   agents:
     assistant:
       instructions: "You are a helpful AI assistant."
       model: gpt-4o-mini
       reflection: true   # opt-in: enables self-critique
   

   Response latency: ~12.3 seconds (8x slower for better quality).

---

How It Works

Gateway agents optimize for chat channel performance with different flow patterns:

Mode	Reflection	Latency	Quality
Fast (default)	false	~1.6s	Direct response
Quality (opt-in)	true	~12.3s	Self-critiqued response

---

Configuration Options

Gateway agents loaded from YAML use chat-optimised defaults:

YAML key	Gateway default	SDK default	Why different
reflection	false	false	Chat channels need sub-second replies
tool_choice	null (auto)	null (auto)	Let LLM decide when to call tools
allow_delegation	false	false	Prevents routing unless opted in

---

Common Patterns

Plain Chat Assistant

agents:
  assistant:
    instructions: "You are a helpful AI assistant."
    model: gpt-4o-mini
    # reflection defaults to false - fast responses

High-Quality Q&A Bot

agents:
  expert:
    instructions: "You are an expert consultant. Think carefully before answering."
    model: gpt-4o
    reflection: true  # opt-in for quality over speed

Mixed Agent Configuration

agents:
  quickhelp:
    instructions: "Handle basic questions quickly."
    model: gpt-4o-mini
    # reflection=false (default) - fast responses
    
  research:
    instructions: "Conduct thorough research and analysis."
    model: gpt-4o
    reflection: true  # enable self-critique for research

---

Best Practices

Default is fast — only enable reflection when quality matters more than latency

Gateway agents default to reflection: false because chat applications prioritize response speed. Enable reflection only for agents handling complex analysis or research tasks.

Measure before opting in: reflection adds 1..max_reflect=3 extra LLM round-trips per message

Each reflection cycle requires additional API calls. For gpt-4o-mini, this increases latency from ~1.6s to ~12.3s. Test your specific use case to validate the quality improvement justifies the speed tradeoff.

Use reflection selectively per agent, not globally

Configure reflection per agent based on their role. Quick support agents should stay fast, while research or analysis agents benefit from reflection.

For background / long-form tasks use the Python SDK directly — it has different defaults suited to that use case

Gateway defaults optimize for interactive chat. For batch processing, long-running tasks, or non-interactive workflows, use the Python SDK directly where different performance characteristics apply.

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Related

Gateway

Gateway configuration and deployment

Self-Reflection

Opt-in self-critique for higher quality

BotOS

Multi-platform bot orchestration

Messaging Bots

Chat platform integrations