Skip to main content
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

StageNameTypical trigger
0DIRECTSimple questions (simple_question)
1HEURISTICFile paths, code blocks
2PLANNEDEdit, test, or build intent
3AUTONOMOUSMulti-step refactor, complex keywords
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
SignalTrigger examples
simple_question”what is”, “define”, “explain”
file_referencesPaths like src/main.py
edit_intent”edit”, “modify”, “fix”
refactor_intent”refactor”, “restructure”
complex_keywords”analyse”, “optimise”, “refactor”

​
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

Pass autonomy=True or an AutonomyConfig — the agent selects the stage automatically during execution.
Call analyze_prompt() to preview complexity without an LLM call when building UIs or routing logic.
Lower the threshold (e.g. 3) for agents that may repeat tool calls; raise it for exploratory coding tasks.
No standalone pipeline objects — escalation, loop guard, and doom-loop detection live on Agent.

Doom Loop Detection

Pattern-based detection of stuck agent loops

Subagent Delegation

Delegate subtasks when autonomy escalates