| name | agent-harness-scaling |
| category | ai_collection |
| version | 1 |
| created | 2026-05-26T00:00:00.000Z |
| source | arXiv:2605.26112 |
| authors | Shangding Gu |
| description | Framework for scaling agentic AI through system-level harness design — context governance, trustworthy memory, dynamic skill routing, and verification — treating the structured execution layer around foundation models as a first-class design object. |
| tags | ["agentic-ai","system-scaling","harness","orchestration","memory","verification","governance"] |
| activation | agent harness, system scaling, context governance, trustworthy memory, skill routing, orchestration loop, verification layer, agentic architecture, long-horizon workflows |
Agent Harness Scaling
Overview
Methodology from arXiv:2605.26112 (May 2026). This paper argues that the next major bottleneck in agentic AI is system scaling, not model scaling. It introduces the concept of scaling the harness: treating the structured execution layer around a foundation model as a first-class object of design, evaluation, and optimization.
Core Insight
Agent performance emerges from the interaction among:
- Foundation model
- Memory substrate
- Context constructor
- Skill-routing layer
- Orchestration loop
- Verification-and-governance layer
Together these form the agent harness, which translates model capability into long-horizon agent behavior.
Three Core Bottlenecks
1. Context Governance
- Managing what information enters the context window
- Balancing relevance, recency, and completeness
- Preventing context pollution and information overload
2. Trustworthy Memory
- Persistent, auditable memory systems
- Memory hygiene — avoiding stale, contradictory, or polluted memories
- Efficient retrieval with provenance tracking
3. Dynamic Skill Routing
- Selecting the right tools/skills for each step
- Adapting routing based on task complexity and context
- Skill versioning and evolution over time
Orchestration & Governance Mechanisms
- Coordination: How components communicate and share state
- Constraint: Safety bounds, resource limits, fallback paths
- Verification: Checking outputs at each step, not just final result
- Safe Evolution: Updating agent components without breaking behavior
Harness-Level Benchmark Metrics
Beyond one-shot task success, measure:
- Trajectory quality: Path efficiency, unnecessary steps
- Memory hygiene: Freshness, consistency, deduplication
- Context efficiency: Signal-to-noise ratio in context
- Communication fidelity: Information preservation between components
- Verification cost: Overhead of correctness checks
- Safe evolution over time: Stability under component updates
When to Apply
