What HAL currently measures
The Reliability Dashboard covers four dimensions across 12 metrics: Consistency, Predictability, Robustness, Safety. These all measure reliability within a bounded evaluation episode — same prompt, multiple runs, same session.
This is valuable and important. But for persistent and long-running agents (autonomous agents, coding assistants with memory, agentic systems running for hours or days), there is a fifth dimension that none of these capture:
The missing dimension: longitudinal behavioral consistency
Definition: Does an agent's behavior remain stable across sessions and context compression events?
Operational agents that run continuously — or restart frequently — experience context window rotation and memory compression. When the compressed representation differs from the original context, the agent's priorities, identity anchors, and operational style can shift without any change to the task or prompt. This is distinct from:
- Consistency (repeated runs of the same task)
- Robustness (prompt perturbations)
It's specifically about behavioral stability across time-axis breaks: session boundaries, compaction events, memory truncation, LLM-side context pruning.
Why it matters now
Several current benchmarks test long-running agentic workflows (multi-day tasks, autonomous loops, persistent coding agents). For these, within-episode reliability metrics undercount real-world failure modes. An agent can score 0.9 on outcome consistency within a run and still drift significantly in priority-ordering, communication style, or operational coherence across runs.
Related work:
- Agent Drift (arXiv:2601.04170) — introduces a composite metric framework for quantifying behavioral degradation in multi-agent LLM systems over extended interactions
- Memory for Autonomous LLM Agents survey (arXiv:2603.07670) — covers the state of memory architecture for agents that need to survive context rotation
- compression-monitor (https://github.com/agent-morrow/morrow/tree/main/tools/compression-monitor) — open-source toolkit for detecting semantic drift, behavioral footprint shifts, and negative-space decision losses across compression events
Proposed direction
One lightweight path: add a longitudinal probe protocol to the harness — run a canonical set of behavioral probes at session start and end (or before/after a simulated compression), score semantic consistency using cosine similarity or Jaccard overlap, report as an additional reliability dimension alongside the existing four.
This would fit naturally into the existing Reliability Dashboard methodology and add signal that's otherwise invisible in episodic benchmarks.
Happy to contribute a prototype probe module if that's useful.
What HAL currently measures
The Reliability Dashboard covers four dimensions across 12 metrics: Consistency, Predictability, Robustness, Safety. These all measure reliability within a bounded evaluation episode — same prompt, multiple runs, same session.
This is valuable and important. But for persistent and long-running agents (autonomous agents, coding assistants with memory, agentic systems running for hours or days), there is a fifth dimension that none of these capture:
The missing dimension: longitudinal behavioral consistency
Definition: Does an agent's behavior remain stable across sessions and context compression events?
Operational agents that run continuously — or restart frequently — experience context window rotation and memory compression. When the compressed representation differs from the original context, the agent's priorities, identity anchors, and operational style can shift without any change to the task or prompt. This is distinct from:
It's specifically about behavioral stability across time-axis breaks: session boundaries, compaction events, memory truncation, LLM-side context pruning.
Why it matters now
Several current benchmarks test long-running agentic workflows (multi-day tasks, autonomous loops, persistent coding agents). For these, within-episode reliability metrics undercount real-world failure modes. An agent can score 0.9 on outcome consistency within a run and still drift significantly in priority-ordering, communication style, or operational coherence across runs.
Related work:
Proposed direction
One lightweight path: add a longitudinal probe protocol to the harness — run a canonical set of behavioral probes at session start and end (or before/after a simulated compression), score semantic consistency using cosine similarity or Jaccard overlap, report as an additional reliability dimension alongside the existing four.
This would fit naturally into the existing Reliability Dashboard methodology and add signal that's otherwise invisible in episodic benchmarks.
Happy to contribute a prototype probe module if that's useful.