Uber LangEffect
Overview
Uber LangEffect is Uber’s internal framework for tracking and reversing agent-caused side effects in production systems. Developed to solve the problem of agents failing mid-task and leaving production systems in inconsistent states, LangEffect adapts the distributed systems Saga pattern for agentic workflows.
Key Facts
- Organization: Uber (internal framework, publicly documented)
- Problem Solved: Atomic rollback of agent-caused side effects across long-running tasks
- Pattern: Saga pattern adapted for LLM agent workflows
- Production Result: 99.2% successful rollback rate over 6 months
- Scope: Production deployment on rider matching, driver assignment, payment systems
Technical Design
Effect Log
Every agent tool call is intercepted by LangEffect middleware and registered in an effect log with two entries:
- The forward action (what was done)
- The compensating transaction (how to undo it)
Compensating Transaction Sources
- Pre-specified: For known operations (cancel payment → refund)
- LLM-generated: For novel operations, with human review for high-value compensations
- Non-compensable flag: Operations that cannot be reversed, requiring human intervention
Rollback Execution
When an agent task fails or is interrupted, LangEffect executes compensating transactions in reverse chronological order, unwinding the agent’s actions to restore a known-good state.
Significance
LangEffect addresses the reliability problem that blocks enterprise adoption of agentic systems: the inability to safely interrupt or recover from failed multi-step agent tasks. The 99.2% rollback success rate demonstrates that production-grade agent reliability is achievable with the right infrastructure.
Limitation
0.8% of cases are non-compensable — external systems processed the agent’s actions beyond reversal. These cases require human intervention, highlighting that some agent actions remain fundamentally irreversible regardless of framework sophistication.