You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Paper: Can Agent Harnesses and Inference Engines Hear Each Other? The HEAR Protocol for Agentic LLM Serving Authors: Conglong Li, et al. Published: 2026-10-05 Effort: high Rationale: gh-aw already separates workflow compilation from pluggable Claude, Copilot, Gemini, and Codex engines, making the adapter boundary a natural protocol surface. A shared schema could let the compiler validate requirements and allow runtimes to schedule parallel agents or manage context more intelligently without embedding engine-specific behavior in workflow markdown.
Verifier and retry safety — Sequence-Aware Acceptance
Paper: Valid Stopping in Adaptive Generator-Verifier Loops Authors: See paper Published: 2026-10-05 Effort: medium Rationale: Repeated compilation repair, evaluation, or safe-output proposal attempts can adapt to a proxy verifier and accumulate false-acceptance risk. gh-aw could first expose attempt histories and configurable stop/escalate hooks, then evaluate statistically valid policies using representative verifier traces before making any default behavior change.
Auditability and human oversight — Evidence-Grounded Behavior Summaries
Paper: What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents Authors: See paper Published: 2026-10-05 Effort: medium Rationale: gh-aw already produces structured workflow activity across tools, sub-agents, and typed writes, but long runs can leave evidence fragmented across logs. A training-free post-processing layer modeled on evidence-grounded behavior grouping could make requirement-to-behavior mismatches and high-impact actions easier to review while preserving links back to original records.
Investigate: Define an optional engine-capability exchange in gh-aw (effort: high)
Investigate: Add a sequence-aware retry-budget policy (effort: medium)
Investigate: Extend audit output with source-linked behavior summaries (effort: medium)
Quick-Win Agentic Prompts
Paste one of these as a new issue or comment to kick off implementation with @copilot:
@copilot Implement: Define an optional engine-capability exchange in gh-aw: the compiled workflow supplies execution intent such as dependency relationships, parallelism, context lifecycle, latency preference, and required features; engine adapters return supported capabilities and structured execution outcomes. in gh-aw's Agentic engine integration component. Rationale: gh-aw already separates workflow compilation from pluggable Claude, Copilot, Gemini, and Codex engines, making the adapter boundary a natural protocol surface. A shared schema could let the compiler validate requirements and allow runtimes to schedule parallel agents or manage context more intelligently without embedding engine-specific behavior in workflow markdown. Source: Can Agent Harnesses and Inference Engines Hear Each Other? The HEAR Protocol for Agentic LLM Serving.
@copilot Implement: Add an experimental retry-budget policy for generator-verifier workflows that records every adaptive verification attempt and applies a calibrated acceptance threshold or escalation rule across the sequence, rather than treating each retry as an independent pass. in gh-aw's Verifier and retry safety component. Rationale: Repeated compilation repair, evaluation, or safe-output proposal attempts can adapt to a proxy verifier and accumulate false-acceptance risk. gh-aw could first expose attempt histories and configurable stop/escalate hooks, then evaluate statistically valid policies using representative verifier traces before making any default behavior change. Source: Valid Stopping in Adaptive Generator-Verifier Loops.
@copilot Implement: Extend gh-aw audit output with a source-linked behavior summary that groups tool calls, sub-agent results, file changes, and safe-output declarations into behaviors, then flags consequential decisions with pointers to their supporting execution evidence. in gh-aw's Auditability and human oversight component. Rationale: gh-aw already produces structured workflow activity across tools, sub-agents, and typed writes, but long runs can leave evidence fragmented across logs. A training-free post-processing layer modeled on evidence-grounded behavior grouping could make requirement-to-behavior mismatches and high-impact actions easier to review while preserving links back to original records. Source: What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents.
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
Uh oh!
There was an error while loading. Please reload this page.
Summary
25 papers screened, 14 relevant, 3 opportunities identified.
Actionable Opportunities
Agentic engine integration — Engine-Capability Exchange
Paper: Can Agent Harnesses and Inference Engines Hear Each Other? The HEAR Protocol for Agentic LLM Serving
Authors: Conglong Li, et al.
Published: 2026-10-05
Effort: high
Rationale: gh-aw already separates workflow compilation from pluggable Claude, Copilot, Gemini, and Codex engines, making the adapter boundary a natural protocol surface. A shared schema could let the compiler validate requirements and allow runtimes to schedule parallel agents or manage context more intelligently without embedding engine-specific behavior in workflow markdown.
Verifier and retry safety — Sequence-Aware Acceptance
Paper: Valid Stopping in Adaptive Generator-Verifier Loops
Authors: See paper
Published: 2026-10-05
Effort: medium
Rationale: Repeated compilation repair, evaluation, or safe-output proposal attempts can adapt to a proxy verifier and accumulate false-acceptance risk. gh-aw could first expose attempt histories and configurable stop/escalate hooks, then evaluate statistically valid policies using representative verifier traces before making any default behavior change.
Auditability and human oversight — Evidence-Grounded Behavior Summaries
Paper: What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents
Authors: See paper
Published: 2026-10-05
Effort: medium
Rationale: gh-aw already produces structured workflow activity across tools, sub-agents, and typed writes, but long runs can leave evidence fragmented across logs. A training-free post-processing layer modeled on evidence-grounded behavior grouping could make requirement-to-behavior mismatches and high-impact actions easier to review while preserving links back to original records.
Papers Analyzed
Next Steps
Quick-Win Agentic Prompts
Paste one of these as a new issue or comment to kick off implementation with
@copilot:All reactions