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Workflow and prompt optimization — Trace-guided workflow harness optimizer
Paper: Emergent Meta-Harness Optimization Authors: See paper Published: 2026-10-06 Effort: medium–high Rationale: EMHO shows that execution experience can improve the surrounding harness without retraining the model. gh-aw already has structured workflows, engine-independent compilation, audit data, and typed outputs, making trace-to-patch optimization direct and testable.
Add an offline gh-aw optimization loop that analyzes failed and successful execution traces, identifies harness-level causes, and proposes bounded edits to prompts, context selection, tool instructions, monitoring, and recovery logic while keeping the model fixed.
Token and context optimization — Replay-gated context compaction evaluation
Add a paired-replay benchmark for gh-aw compaction strategies that resumes identical workflow states once with full pre-compaction context and once with the generated summary, then compares downstream tool behavior and task outcomes.
Paper: AdvSim2Real: Training Web Agents Against Adaptive Prompt Injection in a Web World Model Authors: Sarim Hashmi, Mukul Ranjan, Kshitij Mishra Published: 2026-10-06 Effort: medium Rationale: Static injection fixtures quickly become saturated and can overstate defense quality. Co-evolving task difficulty and attacks would continuously expose engine-specific weaknesses in prompt boundaries, AWF controls, and safe-output enforcement.
Add a gh-aw security evaluation harness that jointly varies benign workflow tasks and untrusted-content injection payloads, retaining attacks only when they flip a task from a policy-compliant success into an unsafe or off-goal outcome.
Paste one of these as a new issue or comment to kick off implementation with @copilot:
@copilot Implement: Add an offline gh-aw optimization loop that analyzes failed and successful execution traces, identifies harness-level causes, and proposes bounded edits to prompts, context selection, tool instructions, monitoring, and recovery logic while keeping the model fixed. in gh-aw's Workflow and prompt optimization component. Rationale: EMHO shows that execution experience can improve the surrounding harness rather than requiring model training. gh-aw already has structured workflows, engine-independent compilation, audit data, and typed outputs, making trace-to-patch optimization unusually direct and testable. Source: Emergent Meta-Harness Optimization.
@copilot Implement: Add a paired-replay benchmark for gh-aw compaction strategies that resumes identical workflow states once with full pre-compaction context and once with the generated summary, then compares downstream tool behavior and task outcomes. in gh-aw's Token and context optimization component. Rationale: TRACE finds that recent behavioral heuristics predict compaction harm only weakly, so triggering or judging compaction from simple trajectory labels is unreliable. Paired replay directly measures causal degradation and can identify summary policies that preserve operational state across engines. Source: Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus.
@copilot Implement: Add a gh-aw security evaluation harness that jointly varies benign workflow tasks and untrusted-content injection payloads, retaining attacks only when they flip a task from a policy-compliant success into an unsafe or off-goal outcome. in gh-aw's firewall/security component. Rationale: Static injection fixtures quickly become saturated and can overstate defense quality. Co-evolving task difficulty and attacks would continuously expose engine-specific weaknesses in prompt boundaries, AWF controls, and safe-output enforcement while preserving the legitimate data and controls agents need. Source: AdvSim2Real: Training Web Agents Against Adaptive Prompt Injection in a Web World Model.
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Summary
25 papers screened, 18 relevant, 3 opportunities identified.
Actionable Opportunities
Workflow and prompt optimization — Trace-guided workflow harness optimizer
Paper: Emergent Meta-Harness Optimization
Authors: See paper
Published: 2026-10-06
Effort: medium–high
Rationale: EMHO shows that execution experience can improve the surrounding harness without retraining the model. gh-aw already has structured workflows, engine-independent compilation, audit data, and typed outputs, making trace-to-patch optimization direct and testable.
Add an offline gh-aw optimization loop that analyzes failed and successful execution traces, identifies harness-level causes, and proposes bounded edits to prompts, context selection, tool instructions, monitoring, and recovery logic while keeping the model fixed.
Token and context optimization — Replay-gated context compaction evaluation
Paper: Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus
Authors: Egor Pakhomov, Erik Nijkamp
Published: 2026-10-06
Effort: medium
Rationale: TRACE finds that recent behavioral heuristics predict compaction harm only weakly. Paired replay directly measures causal degradation and can identify summary policies that preserve operational state across engines.
Add a paired-replay benchmark for gh-aw compaction strategies that resumes identical workflow states once with full pre-compaction context and once with the generated summary, then compares downstream tool behavior and task outcomes.
Firewall/security — Adaptive Prompt-Injection Regression Harness
Paper: AdvSim2Real: Training Web Agents Against Adaptive Prompt Injection in a Web World Model
Authors: Sarim Hashmi, Mukul Ranjan, Kshitij Mishra
Published: 2026-10-06
Effort: medium
Rationale: Static injection fixtures quickly become saturated and can overstate defense quality. Co-evolving task difficulty and attacks would continuously expose engine-specific weaknesses in prompt boundaries, AWF controls, and safe-output enforcement.
Add a gh-aw security evaluation harness that jointly varies benign workflow tasks and untrusted-content injection payloads, retaining attacks only when they flip a task from a policy-compliant success into an unsafe or off-goal outcome.
Papers Analyzed
Next Steps
Quick-Win Agentic Prompts
Paste one of these as a new issue or comment to kick off implementation with
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