Harness Engineering > Model Swaps: What Actually Improves Agent Reliability
Model quality matters. But in real agent systems, reliability is usually won or lost by the harness: context delivery, verification gates, loop control, and trace-driven iteration.
The Core Reframe
Teams often treat agent improvement like a model shopping problem. The operator view is different: the model is the engine, the harness is the control system. If control is weak, capability leaks through avoidable failures — early exits, spec drift, loop traps, and wasted budget.
The practical question is not “which model is smartest?” It is “what behaviors does the harness reliably enforce under stress?”
What Moves Reliability
1) Verification-gated completion
Most weak runs die at “looks right.” Strong harnesses force proof before completion: explicit spec checks, test execution where available, and a mandatory fix loop if verification fails. This replaces confidence-based exits with evidence-based exits.
2) Deterministic context onboarding
Agents burn time rediscovering environment facts. Injecting directory, tool, and constraint context at start cuts that error surface. Less blind search, fewer avoidable path errors, faster convergence.
3) Loop intervention heuristics
Agents can get myopic and repeat local edits against a broken plan. Lightweight loop detection (e.g., repeated edits in the same locus) plus forced reconsideration prompts can recover stalled runs.
4) Reasoning budget scheduling
Uniform max reasoning sounds safe but often hurts throughput under time constraints. A staged policy is usually better: heavier planning, moderate build, heavier final verification. The target is correctness per unit time, not just depth.
Trace-Driven Improvement Loop
Reliability work without traces is mostly superstition. The repeatable loop is:
- capture traces at scale,
- classify failures by type,
- patch harness behavior,
- rerun and compare distribution shifts.
This turns “I think it got better” into measurable engineering.
Operator Checklist
- Add pre-exit verification gate.
- Inject deterministic environment context at run start.
- Detect repeated-edit loops and trigger plan refresh.
- Adopt staged reasoning budgets by phase.
- Track regressions by failure class, not just final score.
Bottom Line
If your agent fails in production, swapping models can help — but harness quality determines whether model capability translates into repeatable execution. Build stronger control loops first.
Reference inspiration: public post by @Vtrivedy10 on harness engineering and benchmark gains.