Escalation Pipeline

Agent-centric progressive escalation from direct response to autonomous mode

The escalation pipeline adjusts execution complexity from simple answers to full autonomous loops — all via Agent(autonomy=True).

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful coding assistant.",
    llm="gpt-4o-mini",
    autonomy=True,
)

stage = agent.get_recommended_stage("Refactor the auth module")
print(stage)  # "autonomous"

response = agent.start("What is Python?")
print(response)

The user sends a prompt; the agent escalates from direct answers to heuristic, planned, or autonomous execution based on task signals.

How It Works

Escalation Stages

Quick Start

1. Enable autonomy

   from praisonaiagents import Agent
   
   agent = Agent(
       instructions="You are a helpful coding assistant.",
       llm="gpt-4o-mini",
       autonomy=True,
   )
   
   # Fast heuristics — no LLM call
   stage = agent.get_recommended_stage("What is Python?")
   signals = agent.analyze_prompt("Refactor the auth module")
   
   print(stage)    # direct
   print(signals)  # {'refactor_intent', 'complex_keywords'}
   
   response = agent.start("What is Python?")
   print(response)

2. Custom configuration

   from praisonaiagents import Agent, AutonomyConfig
   
   agent = Agent(
       instructions="You are a coding assistant.",
       autonomy=AutonomyConfig(
           max_iterations=30,
           doom_loop_threshold=5,
           auto_escalate=True,
       ),
   )
   
   print(agent.autonomy_enabled)
   print(agent.autonomy_config)

Stages and Signals

from praisonaiagents import Agent

agent = Agent(instructions="Assistant", autonomy=True)

agent.get_recommended_stage("What is Python?")           # direct
agent.get_recommended_stage("Read src/main.py")          # heuristic
agent.get_recommended_stage("Fix the bug in auth.py")    # planned
agent.get_recommended_stage("Refactor auth and add tests") # autonomous

CLI

# Simple question — DIRECT stage
praisonai "What is Python?"

# Complex task — escalates to AUTONOMOUS
praisonai "Refactor the auth module and add tests"

praisonai chat --autonomy

Pipeline Components

Under praisonaiagents/escalation/:

• Doom loop detector — identical calls and no-progress patterns
• Loop guard — idempotency-aware tool-call guardrails on every Agent. See Loop Guard.
• Pipeline orchestration — escalation triggers and recovery
• Observability hooks — metrics for escalation events

Best Practices

permissions:
  doom_loop: allow

from praisonaiagents import Agent
from praisonaiagents.permissions import PermissionManager

agent = Agent(
    instructions="Poll the build until it finishes.",
    permission_manager=PermissionManager(rules={"doom_loop": "allow"}),
)

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