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)
   

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Stages and Signals

Stage	Name	Typical trigger
0	DIRECT	Simple questions (simple_question)
1	HEURISTIC	File paths, code blocks
2	PLANNED	Edit, test, or build intent
3	AUTONOMOUS	Multi-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

Signal	Trigger examples
simple_question	”what is”, “define”, “explain”
file_references	Paths 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

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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

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Best Practices

Enable with autonomy=True

Pass autonomy=True or an AutonomyConfig — the agent selects the stage automatically during execution.

Inspect signals before heavy runs

Call analyze_prompt() to preview complexity without an LLM call when building UIs or routing logic.

Tune doom_loop_threshold

Lower the threshold (e.g. 3) for agents that may repeat tool calls; raise it for exploratory coding tasks.

Stay agent-centric

No standalone pipeline objects — escalation, loop guard, and doom-loop detection live on Agent.

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Related

Doom Loop Detection

Pattern-based detection of stuck agent loops

Subagent Delegation

Delegate subtasks when autonomy escalates