AX-Copilot-Codex/docs/CODE_CONTEXT_RELIABILITY_PLAN.md

Code Context Reliability Plan

Update: 2026-04-16 01:37 (KST)

Background

Recent Code tab runs show that the LLM request payload is still growing over time. In the 2026-04-16 00:46:26 to 00:50:52 run, the request size grew from messages=7 to messages=125. That means the failure mode is not "context does not grow at all." The real problem is context fidelity: detailed evidence that the model still needs is being replaced too quickly by previews, repair notes, and low-signal summaries.

The same log window repeatedly shows:

• tool_calls/tool mismatch detected - flattening assistant message
• orphan tool message detected - converting to user
• repeated rereads of nearby files after build failures
• shifting build failures such as MC3089 followed by CS0017 without a stable working set that preserves what was already changed and what remains broken

In short, the current system grows the raw message count but does not preserve a stable working set for long-running code tasks.

Current Findings

1. Workspace context bootstrap is weak on first load

• AX targets:
  • src/AxCopilot/Views/ChatWindow.UtilityPresentation.cs
  • src/AxCopilot/Services/Agent/WorkspaceContextGenerator.cs
• Finding:
  • When .ax-context.md is missing, the first Code request can return before background workspace-context generation becomes useful.
• Impact:
  • Empty-workspace and fresh-project tasks start without a reliable folder or project summary in the early loops.

2. Build and file evidence is compacted too aggressively

• AX targets:
  • src/AxCopilot/Services/Agent/AgentToolResultBudget.cs
  • src/AxCopilot/Services/Agent/ContextCondenser.cs
• Current values:
  • DefaultSoftCharLimit = 900
  • DefaultAggregateBudgetChars = 7_500
  • RecentKeepCount = 6
• Impact:
  • Code tasks lose detailed build, test, and file-read evidence too early and fall back to previews instead of actionable context.

3. Session learning is not a durable code working set

• AX targets:
  • src/AxCopilot/Services/Agent/SessionLearningCollector.cs
  • src/AxCopilot/Services/Agent/AgentLoopService.cs
  • src/AxCopilot/Views/ChatWindow.SystemPromptBuilder.cs
• Finding:
  • Session learnings are injected every loop, but they are not structured strongly enough to lock in:
    • current goal
    • current architecture
    • changed files
    • latest build or test failure
    • next repair target
• Impact:
  • The model must repeatedly reconstruct project state from noisy history instead of reading a stable code-task memory layer.

4. Tool-trace invariant repairs are too common

• AX targets:
  • src/AxCopilot/Services/LlmService.ToolUse.cs
  • src/AxCopilot/Services/Agent/AgentMessageInvariantHelper.cs
  • src/AxCopilot/Services/Agent/AgentLoopService.cs
• Finding:
  • The recent logs show repeated mismatch and orphan corrections.
• Impact:
  • Even if the total message count grows, the semantic chain between assistant reasoning, tool call, and tool result becomes less reliable.

5. There is no Code-specific working-set layer

• AX targets:
  • new service required
  • injection path should go through:
    • AgentLoopLlmRequestPreparationService
    • AgentQueryContextBuilder
    • AgentLoopService
• Finding:
  • The current request mixes raw chat history, session learnings, project context, and workspace context, but it does not maintain a dedicated code-task state ledger.
• Impact:
  • Long-running runs become increasingly inconsistent because the model keeps rediscovering facts that should already be fixed in memory.

External Research Notes

Anthropic Claude Code memory docs

• Claude Code explicitly documents memory files that are auto-loaded at startup and inspectable via /memory.
• Planning implication:
  • AX should have a clearly observable memory hierarchy for Code tasks, including what was auto-loaded and why.
• Source:
  • Anthropic Claude Code memory docs

OpenAI practical guide to building agents

• The guide emphasizes observability, eval baselines, and explicit tool and system design before optimizing agent behavior.
• Planning implication:
  • AX should log the exact context sections that enter each Code request, including what was compacted and why.
• Source:
  • OpenAI practical guide to building agents

SWE-Pruner

• The paper argues that task-aware adaptive pruning outperforms naive fixed truncation for coding agents.
• Planning implication:
  • AX should protect code-task evidence such as latest build failures and changed-file summaries instead of applying mostly size-based pruning.
• Source:
  • SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

claude-code Reference Points

Reference targets:

• claw-code/claw-code-f5a40b86dede580f6543bf8926c9af017eea9409/src/query.ts
• claw-code/claw-code-f5a40b86dede580f6543bf8926c9af017eea9409/src/history.ts
• claw-code/en/concepts/memory-context.md

Observed direction:

• claude-code builds a dedicated messagesForQuery window.
• It stages compaction through boundary filtering, tool-result budgeting, snip, microcompact, and autocompact.
• It treats memory and post-compaction query windows as first-class parts of the request path.

AX already has similar mechanisms, but the Code flow still lacks stronger working-set preservation and cleaner invariant handling.

Remediation Plan

Phase 1. Context observability and bootstrap repair

• Reference targets:
  • claw-code/.../src/query.ts
  • claw-code/en/concepts/memory-context.md
• AX targets:
  • src/AxCopilot/Views/ChatWindow.UtilityPresentation.cs
  • src/AxCopilot/Services/Agent/WorkspaceContextGenerator.cs
  • src/AxCopilot/Services/Agent/AgentQueryContextBuilder.cs
  • src/AxCopilot/Services/Agent/AgentLoopLlmRequestPreparationService.cs
• Work items:
  • guarantee workspace-context generation starts even on first miss
  • log the exact context sections injected into each request
  • add diagnostics for omitted sections
• Done criteria:
  • empty-workspace runs show workspace context generation by loop 2
  • logs show section names, sizes, and compaction status
• Quality scenario:
  • a fresh E:\code WPF scaffolding run should show folder and project context in the first two request cycles

Phase 2. Code working-set memory layer

• Reference targets:
  • claw-code/.../src/query.ts
  • claw-code/.../src/history.ts
  • Anthropic memory docs
• AX targets:
  • new CodeTaskWorkingSetService
  • src/AxCopilot/Services/Agent/SessionLearningCollector.cs
  • src/AxCopilot/Services/Agent/AgentLoopService.cs
  • src/AxCopilot/Services/Agent/AgentQueryContextBuilder.cs
• Work items:
  • maintain a stable structured ledger with:
    • current goal
    • selected architecture
    • changed files
    • latest successful writes
    • open diagnostics
    • next repair target
  • inject it only when changed
  • replace superseded failures with the latest active issue
• Done criteria:
  • long Code runs keep a single coherent working-set block without noisy duplication
  • build and test failures are preserved as part of the working set
• Quality scenario:
  • after fixing MC3089, the run should still remember the earlier structure change while focusing on the new CS0017 entry-point failure

Phase 3. Task-aware pruning and protected evidence

• Reference targets:
  • claw-code/.../src/query.ts
  • SWE-Pruner
• AX targets:
  • src/AxCopilot/Services/Agent/AgentToolResultBudget.cs
  • src/AxCopilot/Services/Agent/ContextCondenser.cs
  • src/AxCopilot/Services/Agent/AgentQueryContextBuilder.cs
• Work items:
  • protect:
    • latest build error block
    • latest test failure block
    • current plan or working set
    • latest folder tree snapshot
    • last N write diffs
  • move from pure char-based truncation toward semantic snapshots
  • tune compaction rules specifically for Code tasks
• Done criteria:
  • active repair evidence survives across loops until superseded
  • older noise shrinks without losing the current failure context
• Quality scenario:
  • a 30-plus-loop Code run should still preserve the latest failure and target files in the request payload

Phase 4. Tool-trace invariant hardening

• Reference targets:
  • claw-code/.../src/query.ts
  • claw-code/.../src/history.ts
• AX targets:
  • src/AxCopilot/Services/LlmService.ToolUse.cs
  • src/AxCopilot/Services/Agent/AgentMessageInvariantHelper.cs
  • src/AxCopilot/Services/Agent/AgentLoopService.cs
• Work items:
  • shift from after-the-fact flattening to pre-request validation and normalization
  • classify mismatch and orphan causes and lock them with regression tests
  • add a final integrity pass before query submission
• Done criteria:
  • standard Code runs approach zero mismatch or orphan repair logs
  • assistant, tool, and tool_result chains remain intact end to end
• Quality scenario:
  • a 50-loop Code run should complete without repeated tool-trace repair events

Phase 5. Encoding hygiene and prompt cleanup

• Reference targets:
  • Anthropic memory docs
  • OpenAI practical guide eval and observability recommendations
• AX targets:
  • src/AxCopilot/Views/ChatWindow.SystemPromptBuilder.cs
  • src/AxCopilot/Services/Agent/SessionLearningCollector.cs
  • active status, prompt, and catalog files
  • AGENTS.md
• Work items:
  • enforce English-only comments in code files
  • rewrite mojibake strings in active prompt paths into English
  • add long-run Code evals to catch prompt and status encoding regressions
• Done criteria:
  • no broken strings remain in active prompt or status paths
  • touched code files keep English comments only
• Quality scenario:
  • Windows Korean environments should show readable build, test, and status output without mojibake feedback loops

Priority

1. Phase 1: bootstrap and observability
2. Phase 2: working-set memory
3. Phase 3: task-aware pruning
4. Phase 4: tool-trace invariants
5. Phase 5: encoding and prompt cleanup

Expected Outcome

• fewer repeated build-failure loops
• better structural consistency for project generation and large edits
• less drift in long-running Code tasks
• fewer quality losses caused by broken strings and low-signal context replacements