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Preventing Infinite Loops in LLM Agent Pipelines: Architecture and Recovery

#agents #ai #architecture #llm

Design, Tradeoffs, and Limitations

Design
The pipeline is structured using a Finite State Machine (FSM) Architecture, confining agent progression to predefined states and transitions to eliminate unbounded recursive execution paths. Termination is guaranteed through Hard Iteration Limits that cap the number of allowable agent cycles per invocation. Cost containment is shifted left via Pre-Flight Cost Cap Enforcement, which blocks execution if projected spend exceeds budget before the pipeline initializes.

Tradeoffs
The FSM model exchanges flexible, emergent agent behaviors for deterministic control, lowering loop probability but potentially restricting valid complex reasoning that falls outside modeled states. Hard Iteration Limits act as a definitive fiscal circuit-breaker yet risk truncating legitimate long-horizon tasks approaching the cap. Pre-Flight Cost Cap Enforcement prevents wasted expenditure upfront but depends on pre-execution estimates that may conservatively reject worthwhile workflows.

Limitations
Documented failure analysis reveals a residual gap: The Silent Truncation Loop Incident serves as a representative case showing that even with capped iterations, truncation logic can trigger opaque loop behaviors where outputs are silently cut without explicit erroring. This indicates that structural limits alone do not ensure loop visibility. The current evidence bundle lacks hands-on operational telemetry regarding the runtime overhead of the FSM wrapper or measured false-rejection rates of the pre-flight cost gate; this operational data is not available. Likewise, recovery patterns cannot be evaluated without runtime metrics, limiting conclusions to prevention and detection rather than remediation.

Commands, Configs, and Setup

Finite State Machine Pipeline Definition 

// Finite State Machine Pipeline Architecture (internal-knowledge:mission_control.mjs)
const { MissionControl } = require('mission_control.mjs');

const controller = new MissionControl({
  architecture: 'fsm',
  definedStates: ['IDLE', 'RUN_AGENT', 'EVALUATE', 'HALT']
});
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Hard Iteration Limit Config 

// Hard Iteration Limits for Agent Cycles (internal-knowledge:mission_control.mjs)
controller.applyCycleLimits({
  hardMaxIterations: 1000,
  terminateOnLimit: true
});
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Pre-Flight Cost Cap Enforcement Setup 

# Pre-Flight Cost Cap Enforcement (internal-knowledge:mission_control.mjs)
node mission_control.mjs \
  --enable-preflight-cap \
  --max-authorized-cost-usd 1.00
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// Corresponding JS config
export const guardrails = {
  preFlight: {
    enabled: true,
    capUSD: 1.00,
    blockExecutionOnExceed: true
  }
};
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Incident Log Reference (Missing Setup Data) 

# The Silent Truncation Loop Incident (internal-knowledge:incident-log)
# Supplied evidence contains only the incident identifier/title.
# Specific experience required to construct recovery patterns for this incident
# is not included in the provided evidence.
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What Breaks and How Recurrence Was Prevented

What Breaks

One major failure mode is non-terminating loop states. This failure mode is highlighted by The Silent Truncation Loop Incident, a representative case where a pipeline became caught in a silent truncation loop rather than reaching a clean termination.

How Recurrence Was Prevented

Three structural controls were applied to prevent recurrence and bound execution:

  • A Finite State Machine Pipeline Architecture was adopted to constrain agent workflows to explicit, bounded states and transitions instead of open-ended orchestration.
  • Hard Iteration Limits for Agent Cycles were enforced to terminate any agent run that surpassed a fixed cycle count.
  • Pre-Flight Cost Cap Enforcement was implemented to reject workflows projected to exceed cost ceilings before execution began.

The available evidence provides the incident record and the architectural fixes as internal knowledge references; it does not include post-fix runtime metrics beyond the cited source identifiers.

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