- Block REST API endpoints for label creation at the bash level for all agents.
- Restrict `forgejo_create_label` and related MCP tools for all agents.
- Restrict `forgejo_add_issue_labels` to only the `forgejo-label-manager`.
- Ensure all label operations are centralized through the `forgejo-label-manager`.
- Update agent definitions to use the label manager instead of direct API calls or MCP tools for adding labels.
This prevents agents from creating new project-level labels and enforces the use of organization-level labels, resolving the issue of duplicate labels being created.
Add explicit clone isolation protocols and warnings to prevent agents
from manipulating the local repository in /app. This ensures:
- Agents use isolated /tmp/ clones for all source code operations
- No interference between parallel agents
- No disruption to developer's local work environment
- No conflicts from branch changes or file modifications
Updated agents:
- Core implementation agents (implementer, build, plan)
- Quality gate agents (lint-fixer, typecheck-fixer, test-fixer, etc.)
- Test writing agents (behave-tester, unit-test-runner, coverage-improver)
- Analysis agents (difficulty-evaluator, fix-pr)
- Special cases (build-opencode with .opencode/ exception)
Each agent now includes prominent warnings and proper isolation protocols
with detailed explanations of why clone isolation is critical for
system stability.
Updated multiple agents to understand and properly handle TDD (Test-Driven
Development) tags as documented in CONTRIBUTING.md. This prevents confusion
when agents encounter tests with @tdd_expected_fail that invert their behavior.
Key changes:
- Test writers (behave-tester, robot-tester) now understand when to use TDD tags
- Implementers know to remove @tdd_expected_fail tags when fixing bugs
- Test-fixer won't try to "fix" correctly passing TDD tests
- PR reviewers check for proper TDD tag removal in bug fix PRs
- Human liaison can explain TDD tags to confused developers
- Coverage improver avoids modifying TDD tests
- Reference reader includes TDD tag info in summaries
This ensures all agents work correctly with the TDD workflow where tests are
written before bug fixes and use special tags to prove bugs exist.
- Remove maximum cap (16) on CA_MAX_PARALLEL_WORKERS in resources.yaml
- Can now be set to any positive value (32, 64, etc.)
- Only minimum validation remains (must be > 0)
- Remove dynamic backpressure/throttling from implementation-orchestrator
- Dispatch always runs at full configured speed
- Resource monitoring remains for visibility only
- No automatic reduction of slots_available based on failures
- Convert system-watchdog from auto-degradation to monitoring + suggestions
- Renamed DEGRADATION_THRESHOLDS to HEALTH_THRESHOLDS
- Removed apply_system_degradation() and check_degradation_recovery()
- Changed findings to include suggestions instead of actions
- Watchdog now reports issues with fix recommendations
- No automatic throttling or pausing of agents
The system now operates at maximum configured speed at all times,
with the watchdog providing diagnostic insights when issues arise.
BREAKING CHANGE: New escalation architecture eliminates duplicate agent code
Key changes:
- Add tier selector agents (tier-haiku, tier-codex, tier-sonnet, tier-opus)
that set the model and invoke worker agents
- Create model-agnostic implementer.md that inherits model from caller
- Update unit-test-runner and typecheck-fixer to support escalation
- Add quality-gate-escalator to manage escalation for quality fixers
- Create subtask-loop-v2.md demonstrating the new architecture
- Document the new architecture in escalation-architecture-proposal.md
Benefits:
- Eliminates 90% code duplication across tier-specific agents
- Single source of truth for implementation logic
- Easy to add new tiers or modify escalation paths
- Consistent behavior across all model tiers
- Quality gate fixers now support full escalation starting from haiku
The architecture leverages the fact that agents without a model specification
inherit the model from their caller, allowing thin tier selectors to control
which model executes the actual work.
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