- Add sanitize_for_prompt() function to security module
- Remove suspicious control characters (except whitespace)
- Detect and log common prompt injection patterns
- Escape dangerous XML-like tags used for prompt manipulation
- Truncate user content to max length (default 50000 chars)
- Integrate sanitization in parser before building LLM prompts
- Add comprehensive test suite (12 new tests)
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Log ERROR when queue corruption detected with error details
- Create timestamped backup before discarding corrupted data
- Add comprehensive tests for corruption handling
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement circuit breaker pattern to prevent infinite retry loops on
repeated failures (SEC-ORCH-7). The circuit breaker tracks consecutive
failures and opens after a threshold is reached, blocking further
requests until a cooldown period elapses.
Circuit breaker states:
- CLOSED: Normal operation, requests pass through
- OPEN: After N consecutive failures, all requests blocked
- HALF_OPEN: After cooldown, allow one test request
Changes:
- Add circuit_breaker.py with CircuitBreaker class
- Integrate circuit breaker into Coordinator.start() loop
- Integrate circuit breaker into OrchestrationLoop.start() loop
- Integrate per-agent circuit breakers into ContextMonitor
- Add comprehensive tests for circuit breaker behavior
- Log state transitions and circuit breaker stats on shutdown
Configuration (defaults):
- failure_threshold: 5 consecutive failures
- cooldown_seconds: 30 seconds
Refs #338
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement session rotation that spawns fresh agents when context reaches
95% threshold.
TDD Process:
1. RED: Write comprehensive tests (all initially fail)
2. GREEN: Implement trigger_rotation method (all tests pass)
Changes:
- Add SessionRotation dataclass to track rotation metrics
- Implement trigger_rotation method in ContextMonitor
- Add 6 new unit tests covering all acceptance criteria
Rotation process:
1. Get current context usage metrics
2. Close current agent session
3. Spawn new agent with same type
4. Transfer next issue to new agent
5. Log rotation event with metrics
Test Results:
- All 47 tests pass (34 context_monitor + 13 context_compaction)
- 97% coverage on context_monitor.py (exceeds 85% requirement)
- 97% coverage on context_compaction.py (exceeds 85% requirement)
Prevents context exhaustion by starting fresh when compaction is insufficient.
Acceptance Criteria (All Met):
✓ Rotation triggered at 95% context threshold
✓ Current session closed cleanly
✓ New agent spawned with same type
✓ Next issue transferred to new agent
✓ Rotation logged with session IDs and context metrics
✓ Unit tests with 85%+ coverage
Fixes#152
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement the main orchestration loop that coordinates all components:
- Queue processing with priority sorting (issues by number)
- Integration with ContextMonitor for tracking agent context usage
- Integration with QualityOrchestrator for running quality gates
- Integration with ForcedContinuationService for rejection prompts
- Metrics tracking (processed_count, success_count, rejection_count)
- Graceful start/stop with proper lifecycle management
- Error handling at all levels (spawn, context, quality, continuation)
The OrchestrationLoop flow:
1. Read issue queue (priority sorted by issue number)
2. Mark issue as in progress
3. Spawn agent (stub implementation for Phase 0)
4. Check context usage via ContextMonitor
5. Run quality gates via QualityOrchestrator
6. On approval: mark complete, increment success count
7. On rejection: generate continuation prompt, increment rejection count
99% test coverage for coordinator.py (183 statements, 2 missed).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implements the Coordinator class with main orchestration loop:
- Async loop architecture with configurable poll interval
- process_queue() method gets next ready issue and spawns agent (stub)
- Graceful shutdown handling with stop() method
- Error handling that allows loop to continue after failures
- Logging for all actions (start, stop, processing, errors)
- Integration with QueueManager from #159
- Active agent tracking for future agent management
Configuration settings added:
- COORDINATOR_POLL_INTERVAL (default: 5.0s)
- COORDINATOR_MAX_CONCURRENT_AGENTS (default: 10)
- COORDINATOR_ENABLED (default: true)
Tests: 27 new tests covering all acceptance criteria
Coverage: 92% overall (100% for coordinator.py)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add Capability enum (HIGH, MEDIUM, LOW) for agent difficulty levels
- Add AgentName enum for all 5 agents (opus, sonnet, haiku, glm, minimax)
- Implement AgentProfile data structure with validation
- context_limit: max tokens for context window
- cost_per_mtok: cost per million tokens (0 for self-hosted)
- capabilities: list of difficulty levels the agent handles
- best_for: description of optimal use cases
- Define profiles for all 5 agents with specifications:
- Anthropic models (opus, sonnet, haiku): 200K context, various costs
- Self-hosted models (glm, minimax): 128K context, free
- Implement get_agent_profile() function for profile lookup
- Add comprehensive test suite (37 tests, 100% coverage)
- Profile data structure validation
- All 5 predefined profiles exist and are correct
- Capability enum and AgentName enum tests
- Best_for validation and capability matching
- Consistency checks across profiles
Fixes#144
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements ContextMonitor class with real-time token usage tracking:
- COMPACT_THRESHOLD at 0.80 (80% triggers compaction)
- ROTATE_THRESHOLD at 0.95 (95% triggers rotation)
- Poll Claude API for context usage
- Return appropriate ContextAction based on thresholds
- Background monitoring loop (10-second polling)
- Log usage over time
- Error handling and recovery
Added ContextUsage model for tracking agent token consumption.
Tests:
- 25 test cases covering all functionality
- 100% coverage for context_monitor.py and models.py
- Mocked API responses for different usage levels
- Background monitoring and threshold detection
- Error handling verification
Quality gates:
- Type checking: PASS (mypy)
- Linting: PASS (ruff)
- Tests: PASS (25/25)
- Coverage: 100% for new files, 95.43% overall
Fixes#155
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
