2026-01-12 03:32:47 +00:00
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< h1 id = "task-agent--documentation-manager" > < a class = "header" href = "#task-agent--documentation-manager" > Task, Agent & Documentation Manager< / a > < / h1 >
< h2 id = "multi-agent-task-orchestration--documentation-sync" > < a class = "header" href = "#multi-agent-task-orchestration--documentation-sync" > Multi-Agent Task Orchestration & Documentation Sync< / a > < / h2 >
< p > < strong > Status< / strong > : Production Ready (v1.2.0)
< strong > Date< / strong > : January 2026< / p >
< hr / >
< h2 id = "-overview" > < a class = "header" href = "#-overview" > 🎯 Overview< / a > < / h2 >
< p > System that:< / p >
< ol >
< li > < strong > Manages tasks< / strong > in multi-agent workflow< / li >
< li > < strong > Assigns agents< / strong > automatically based on expertise< / li >
< li > < strong > Coordinates execution< / strong > in parallel with approval gates< / li >
< li > < strong > Extracts decisions< / strong > as Architecture Decision Records (ADRs)< / li >
< li > < strong > Maintains documentation< / strong > automatically synchronized< / li >
< / ol >
< hr / >
< h2 id = "-task-structure" > < a class = "header" href = "#-task-structure" > 📋 Task Structure< / a > < / h2 >
< h3 id = "task-metadata" > < a class = "header" href = "#task-metadata" > Task Metadata< / a > < / h3 >
< p > Tasks are stored in SurrealDB with the following structure:< / p >
< pre > < code class = "language-toml" > [task]
id = "task-089"
type = "feature" # feature | bugfix | enhancement | tech-debt
title = "Implement learning profiles"
description = "Agent expertise tracking with recency bias"
[status]
state = "in-progress" # todo | in-progress | review | done | archived
progress = 60 # 0-100%
created_at = "2026-01-11T10:15:30Z"
updated_at = "2026-01-11T14:30:22Z"
[assignment]
priority = "high" # high | medium | low
assigned_agent = "developer" # Or null if unassigned
assigned_team = "infrastructure"
[estimation]
estimated_hours = 8
actual_hours = null # Updated when complete
[context]
related_tasks = ["task-087", "task-088"]
blocking_tasks = []
blocked_by = []
< / code > < / pre >
< h3 id = "task-lifecycle" > < a class = "header" href = "#task-lifecycle" > Task Lifecycle< / a > < / h3 >
< pre > < code > ┌─────────┐ ┌──────────────┐ ┌────────┐ ┌──────────┐
│ TODO │────▶│ IN-PROGRESS │────▶│ REVIEW │────▶│ DONE │
└─────────┘ └──────────────┘ └────────┘ └──────────┘
△ │
│ │
└───────────── ARCHIVED ◀───────────┘
< / code > < / pre >
< hr / >
< h2 id = "-agent-assignment" > < a class = "header" href = "#-agent-assignment" > 🤖 Agent Assignment< / a > < / h2 >
< h3 id = "automatic-selection" > < a class = "header" href = "#automatic-selection" > Automatic Selection< / a > < / h3 >
< p > When a task is created, SwarmCoordinator assigns the best agent:< / p >
< ol >
< li > < strong > Capability Matching< / strong > : Filter agents by role matching task type< / li >
< li > < strong > Learning Profile Lookup< / strong > : Get expertise scores for task-type< / li >
< li > < strong > Load Balancing< / strong > : Check current agent load (tasks in progress)< / li >
< li > < strong > Scoring< / strong > : < code > final_score = 0.3*load + 0.5*expertise + 0.2*confidence< / code > < / li >
< li > < strong > Notification< / strong > : Agent receives job via NATS JetStream< / li >
< / ol >
< h3 id = "agent-roles" > < a class = "header" href = "#agent-roles" > Agent Roles< / a > < / h3 >
< div class = "table-wrapper" > < table > < thead > < tr > < th > Role< / th > < th > Specialization< / th > < th > Primary Tasks< / th > < / tr > < / thead > < tbody >
< tr > < td > < strong > Architect< / strong > < / td > < td > System design< / td > < td > Feature planning, ADRs, design reviews< / td > < / tr >
< tr > < td > < strong > Developer< / strong > < / td > < td > Implementation< / td > < td > Code generation, refactoring, debugging< / td > < / tr >
< tr > < td > < strong > Reviewer< / strong > < / td > < td > Quality assurance< / td > < td > Code review, test coverage, style checks< / td > < / tr >
< tr > < td > < strong > Tester< / strong > < / td > < td > QA & Benchmarks< / td > < td > Test suite, performance benchmarks< / td > < / tr >
< tr > < td > < strong > Documenter< / strong > < / td > < td > Documentation< / td > < td > Guides, API docs, README updates< / td > < / tr >
< tr > < td > < strong > Marketer< / strong > < / td > < td > Marketing content< / td > < td > Blog posts, case studies, announcements< / td > < / tr >
< tr > < td > < strong > Presenter< / strong > < / td > < td > Presentations< / td > < td > Slides, deck creation, demo scripts< / td > < / tr >
< tr > < td > < strong > DevOps< / strong > < / td > < td > Infrastructure< / td > < td > CI/CD setup, deployment, monitoring< / td > < / tr >
< tr > < td > < strong > Monitor< / strong > < / td > < td > Health & Alerting< / td > < td > System monitoring, alerts, incident response< / td > < / tr >
< tr > < td > < strong > Security< / strong > < / td > < td > Compliance & Audit< / td > < td > Code security, access control, compliance< / td > < / tr >
< tr > < td > < strong > ProjectManager< / strong > < / td > < td > Coordination< / td > < td > Roadmap, tracking, milestone management< / td > < / tr >
< tr > < td > < strong > DecisionMaker< / strong > < / td > < td > Conflict Resolution< / td > < td > Tie-breaking, escalation, ADR creation< / td > < / tr >
< / tbody > < / table >
< / div >
< hr / >
< h2 id = "-multi-agent-workflow-execution" > < a class = "header" href = "#-multi-agent-workflow-execution" > 🔄 Multi-Agent Workflow Execution< / a > < / h2 >
< h3 id = "sequential-workflow-phases" > < a class = "header" href = "#sequential-workflow-phases" > Sequential Workflow (Phases)< / a > < / h3 >
< pre > < code > Phase 1: Design
└─ Architect creates ADR
└─ Move to Phase 2 (auto on completion)
Phase 2: Development
└─ Developer implements
└─ (Parallel) Documenter writes guide
└─ Move to Phase 3
Phase 3: Review
└─ Reviewer checks code quality
└─ Security audits for compliance
└─ If approved: Move to Phase 4
└─ If rejected: Back to Phase 2
Phase 4: Testing
└─ Tester creates test suite
└─ Tester runs benchmarks
└─ If passing: Move to Phase 5
└─ If failing: Back to Phase 2
Phase 5: Completion
└─ DevOps deploys
└─ Monitor sets up alerts
└─ ProjectManager marks done
< / code > < / pre >
< h3 id = "parallel-coordination" > < a class = "header" href = "#parallel-coordination" > Parallel Coordination< / a > < / h3 >
< p > Multiple agents work simultaneously when independent:< / p >
< pre > < code > Task: "Add learning profiles"
├─ Architect (ADR) ▶ Created in 2h
├─ Developer (Code) ▶ Implemented in 8h
│ ├─ Reviewer (Review) ▶ Reviewed in 1h (parallel)
│ └─ Documenter (Guide) ▶ Documented in 2h (parallel)
│
└─ Tester (Tests) ▶ Tests in 3h
└─ Security (Audit) ▶ Audited in 1h (parallel)
< / code > < / pre >
< h3 id = "approval-gates" > < a class = "header" href = "#approval-gates" > Approval Gates< / a > < / h3 >
< p > Critical decision points require manual approval:< / p >
< ul >
< li > < strong > Security Gate< / strong > : Must approve if code touches auth/secrets< / li >
< li > < strong > Breaking Changes< / strong > : Architect approval required< / li >
< li > < strong > Production Deployment< / strong > : DevOps + ProjectManager approval< / li >
< li > < strong > Major Refactoring< / strong > : Architect + Lead Developer approval< / li >
< / ul >
< hr / >
< h2 id = "-decision-extraction-adrs" > < a class = "header" href = "#-decision-extraction-adrs" > 📝 Decision Extraction (ADRs)< / a > < / h2 >
< p > Every design decision is automatically captured:< / p >
< h3 id = "adr-template" > < a class = "header" href = "#adr-template" > ADR Template< / a > < / h3 >
< pre > < code class = "language-markdown" > # ADR-042: Learning-Based Agent Selection
## Context
Previous agent assignment used simple load balancing (min tasks),
ignoring historical performance data. This led to poor agent-task matches.
## Decision
Implement per-task-type learning profiles with recency bias.
### Key Points
- Success rate weighted by recency (7-day window, 3×
- Confidence scoring prevents small-sample overfitting
- Supports adaptive recovery from temporary degradation
## Consequences
**Positive**:
- 30-50% improvement in task success rate
- Agents improve continuously
**Negative**:
- Requires KG data collection (startup period)
- Learning period ~20 tasks per task-type
## Alternatives Considered
1. Rule-based routing (rejected: no learning)
2. Pure random assignment (rejected: no improvement)
3. Rolling average (rejected: no recency bias)
## Decision Made
Option A: Learning profiles with recency bias
< / code > < / pre >
< h3 id = "adr-extraction-process" > < a class = "header" href = "#adr-extraction-process" > ADR Extraction Process< / a > < / h3 >
< ol >
< li > < strong > Automatic< / strong > : Each task completion generates execution record< / li >
< li > < strong > Learning< / strong > : If decision had trade-offs, extract as ADR candidate< / li >
< li > < strong > Curation< / strong > : ProjectManager/Architect reviews and approves< / li >
2026-02-17 23:15:12 +00:00
< li > < strong > Archival< / strong > : Stored in docs/adrs/ (numbered, immutable)< / li >
2026-01-12 03:32:47 +00:00
< / ol >
< hr / >
< h2 id = "-documentation-synchronization" > < a class = "header" href = "#-documentation-synchronization" > 📚 Documentation Synchronization< / a > < / h2 >
< h3 id = "automatic-updates" > < a class = "header" href = "#automatic-updates" > Automatic Updates< / a > < / h3 >
< p > When tasks complete, documentation is auto-updated:< / p >
< div class = "table-wrapper" > < table > < thead > < tr > < th > Task Type< / th > < th > Auto-Updates< / th > < / tr > < / thead > < tbody >
< tr > < td > Feature< / td > < td > CHANGELOG.md, feature overview, API docs< / td > < / tr >
< tr > < td > Bugfix< / td > < td > CHANGELOG.md, troubleshooting guide< / td > < / tr >
< tr > < td > Tech-Debt< / td > < td > Architecture docs, refactoring guide< / td > < / tr >
< tr > < td > Enhancement< / td > < td > Feature docs, user guide< / td > < / tr >
< tr > < td > Documentation< / td > < td > Indexed in RAG, updated in search< / td > < / tr >
< / tbody > < / table >
< / div >
< h3 id = "documentation-lifecycle" > < a class = "header" href = "#documentation-lifecycle" > Documentation Lifecycle< / a > < / h3 >
< pre > < code > Task Created
│
▼
Documentation Context Extracted
│
├─ Decision/ADR created
├─ Related docs identified
└─ Change summary prepared
│
▼
Task Execution
│
├─ Code generated
├─ Tests created
└─ Examples documented
│
▼
Task Complete
│
├─ ADR finalized
├─ Docs auto-generated
├─ CHANGELOG entry created
└─ Search index updated (RAG)
│
▼
Archival (if stale)
│
└─ Moved to docs/archive/
(kept for historical reference)
< / code > < / pre >
< hr / >
< h2 id = "-search--retrieval-rag-integration" > < a class = "header" href = "#-search--retrieval-rag-integration" > 🔍 Search & Retrieval (RAG Integration)< / a > < / h2 >
< h3 id = "document-indexing" > < a class = "header" href = "#document-indexing" > Document Indexing< / a > < / h3 >
< p > All generated documentation is indexed for semantic search:< / p >
< ul >
< li > < strong > Architecture decisions< / strong > (ADRs)< / li >
< li > < strong > Feature guides< / strong > (how-tos)< / li >
< li > < strong > Code examples< / strong > (patterns)< / li >
< li > < strong > Execution history< / strong > (knowledge graph)< / li >
< / ul >
< h3 id = "query-examples" > < a class = "header" href = "#query-examples" > Query Examples< / a > < / h3 >
< p > User asks: "How do I implement learning profiles?"< / p >
< p > System searches:< / p >
< ol >
< li > ADRs mentioning "learning"< / li >
< li > Implementation guides with "learning"< / li >
< li > Execution history with similar task type< / li >
< li > Code examples for "learning profiles"< / li >
< / ol >
< p > Returns ranked results with sources.< / p >
< hr / >
< h2 id = "-metrics--monitoring" > < a class = "header" href = "#-metrics--monitoring" > 📊 Metrics & Monitoring< / a > < / h2 >
< h3 id = "task-metrics" > < a class = "header" href = "#task-metrics" > Task Metrics< / a > < / h3 >
< ul >
< li > < strong > Success Rate< / strong > : % of tasks completed successfully< / li >
< li > < strong > Cycle Time< / strong > : Average time from todo → done< / li >
< li > < strong > Agent Utilization< / strong > : Tasks per agent per role< / li >
< li > < strong > Decision Quality< / strong > : ADRs implemented vs. abandoned< / li >
< / ul >
< h3 id = "agent-metrics-per-role" > < a class = "header" href = "#agent-metrics-per-role" > Agent Metrics (per role)< / a > < / h3 >
< ul >
< li > < strong > Task Success Rate< / strong > : % tasks completed successfully< / li >
< li > < strong > Learning Curve< / strong > : Expert improvement over time< / li >
< li > < strong > Cost per Task< / strong > : Average LLM spend per completed task< / li >
< li > < strong > Task Coverage< / strong > : Breadth of task-types handled< / li >
< / ul >
< h3 id = "documentation-metrics" > < a class = "header" href = "#documentation-metrics" > Documentation Metrics< / a > < / h3 >
< ul >
< li > < strong > Coverage< / strong > : % of features documented< / li >
< li > < strong > Freshness< / strong > : Days since last update< / li >
< li > < strong > Usage< / strong > : Search queries hitting each doc< / li >
< li > < strong > Accuracy< / strong > : User feedback on doc correctness< / li >
< / ul >
< hr / >
< h2 id = "-implementation-details" > < a class = "header" href = "#-implementation-details" > 🏗️ Implementation Details< / a > < / h2 >
< h3 id = "surrealdb-schema" > < a class = "header" href = "#surrealdb-schema" > SurrealDB Schema< / a > < / h3 >
< pre > < code class = "language-sql" > -- Tasks table
DEFINE TABLE tasks SCHEMAFULL;
DEFINE FIELD id ON tasks TYPE string;
DEFINE FIELD type ON tasks TYPE string;
DEFINE FIELD state ON tasks TYPE string;
DEFINE FIELD assigned_agent ON tasks TYPE option< string> ;
-- Executions (for learning)
DEFINE TABLE executions SCHEMAFULL;
DEFINE FIELD task_id ON executions TYPE string;
DEFINE FIELD agent_id ON executions TYPE string;
DEFINE FIELD success ON executions TYPE bool;
DEFINE FIELD duration_ms ON executions TYPE number;
DEFINE FIELD cost_cents ON executions TYPE number;
-- ADRs table
DEFINE TABLE adrs SCHEMAFULL;
DEFINE FIELD id ON adrs TYPE string;
DEFINE FIELD task_id ON adrs TYPE string;
DEFINE FIELD title ON adrs TYPE string;
DEFINE FIELD status ON adrs TYPE string; -- draft|approved|archived
< / code > < / pre >
< h3 id = "nats-topics" > < a class = "header" href = "#nats-topics" > NATS Topics< / a > < / h3 >
< ul >
< li > < code > tasks.{type}.{priority}< / code > — Task assignments< / li >
< li > < code > agents.{role}.ready< / code > — Agent heartbeats< / li >
< li > < code > agents.{role}.complete< / code > — Task completion< / li >
< li > < code > adrs.created< / code > — New ADR events< / li >
< li > < code > docs.updated< / code > — Documentation changes< / li >
< / ul >
< hr / >
< h2 id = "-key-design-patterns" > < a class = "header" href = "#-key-design-patterns" > 🎯 Key Design Patterns< / a > < / h2 >
< h3 id = "1-event-driven-coordination" > < a class = "header" href = "#1-event-driven-coordination" > 1. Event-Driven Coordination< / a > < / h3 >
< ul >
< li > Task creation → Agent assignment (async via NATS)< / li >
< li > Task completion → Documentation update (eventual consistency)< / li >
< li > No direct API calls between services (loosely coupled)< / li >
< / ul >
< h3 id = "2-learning-from-execution-history" > < a class = "header" href = "#2-learning-from-execution-history" > 2. Learning from Execution History< / a > < / h3 >
< ul >
< li > Every task stores execution metadata (success, duration, cost)< / li >
< li > Learning profiles updated from execution data< / li >
< li > Better assignments improve continuously< / li >
< / ul >
< h3 id = "3-decision-extraction" > < a class = "header" href = "#3-decision-extraction" > 3. Decision Extraction< / a > < / h3 >
< ul >
< li > Design decisions captured as ADRs< / li >
< li > Immutable record of architectural rationale< / li >
< li > Serves as organizational memory< / li >
< / ul >
< h3 id = "4-graceful-degradation" > < a class = "header" href = "#4-graceful-degradation" > 4. Graceful Degradation< / a > < / h3 >
< ul >
< li > NATS offline: In-memory queue fallback< / li >
< li > Agent unavailable: Task re-assigned to next best< / li >
< li > Doc generation failed: Manual entry allowed< / li >
< / ul >
< hr / >
< h2 id = "-related-documentation" > < a class = "header" href = "#-related-documentation" > 📚 Related Documentation< / a > < / h2 >
< ul >
< li > < strong > < a href = "vapora-architecture.html" > VAPORA Architecture< / a > < / strong > — System overview< / li >
< li > < strong > < a href = "agent-registry-coordination.html" > Agent Registry & Coordination< / a > < / strong > — Agent patterns< / li >
< li > < strong > < a href = "multi-agent-workflows.html" > Multi-Agent Workflows< / a > < / strong > — Workflow execution< / li >
< li > < strong > < a href = "multi-ia-router.html" > Multi-IA Router< / a > < / strong > — LLM provider selection< / li >
< li > < strong > < a href = "roles-permissions-profiles.html" > Roles, Permissions & Profiles< / a > < / strong > — RBAC< / li >
< / ul >
< hr / >
< p > < strong > Status< / strong > : ✅ Production Ready
< strong > Version< / strong > : 1.2.0
< strong > Last Updated< / strong > : January 2026< / p >
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