# 🗺️ Flock Roadmap to 1.0

**Building Enterprise Infrastructure for AI Agents**

This roadmap outlines Flock's path from v0.5.0 (production-ready core) to v1.0 (enterprise-complete) by Q3 2025.

---

## ✅ What's Already Production-Ready (v0.5.0)

### Core Framework
- [x] Blackboard orchestrator with typed artifacts
- [x] Declarative agent subscriptions (no graph wiring)
- [x] Parallel + sequential execution (automatic)
- [x] Zero-trust security (5 visibility types)
- [x] Circuit breakers and feedback loop prevention
- [x] 743 tests with 77.65% coverage (86-100% on critical paths)

### Observability
- [x] OpenTelemetry distributed tracing
- [x] DuckDB trace storage (AI-queryable)
- [x] Real-time dashboard with WebSocket streaming
- [x] 7-mode trace viewer (Timeline, RED metrics, Dependencies, SQL)
- [x] Service filtering and CSV export

### Developer Experience
- [x] MCP integration (Model Context Protocol)
- [x] Best-of-N execution
- [x] Exclusive delivery (exactly-once)
- [x] Batch processing and join operations
- [x] Conditional consumption (`where=lambda`)
- [x] Rich console output and FastAPI service

---

## 🚀 Q1 2025: Production Hardening

**Target: End of Q1 2025 (January - March)**

### 🔴 Priority 1: Enterprise Persistence

**Problem:** The blackboard currently lives in memory. Production systems need durable state.

**Solution:**

```python
# Redis persistence (high-throughput, low-latency)
flock = Flock(
    "openai/gpt-4.1",
    persistence=RedisPersistence(
        url="redis://cluster:6379",
        ttl=timedelta(hours=24),
        retention_policy=RetentionPolicy.ARCHIVE_COMPLETED
    )
)

# PostgreSQL persistence (transactional, queryable)
flock = Flock(
    "openai/gpt-4.1",
    persistence=PostgresPersistence(
        connection="postgresql://...",
        schema="flock_production",
        enable_audit_log=True
    )
)
```

**Why this matters:**
- Agent crashes? State persists, agents resume
- Multi-region deployments? Shared blackboard state
- Compliance? Full audit trail in RDBMS
- Analytics? SQL queries on artifact history

**Target:** Redis by end of January, Postgres by mid-February

---

### 🔴 Priority 1: Retry & Error Handling

**Problem:** Production systems fail. Flock needs sophisticated retry mechanisms.

**Solution:**

```python
# Exponential backoff with jitter
agent = flock.agent("processor").retry(
    strategy=ExponentialBackoff(
        initial_delay=timedelta(seconds=1),
        max_delay=timedelta(minutes=5),
        jitter=True,
        max_attempts=5
    ),
    on=[RateLimitError, TimeoutError]  # Selective retry
)

# Dead letter queue for poison messages
agent = flock.agent("processor").retry(
    max_attempts=3,
    dead_letter=DeadLetterQueue(
        destination="failed_artifacts",
        include_trace=True,
        alert_webhook="https://alerts.example.com"
    )
)

# Circuit breaker per agent (not just global)
agent = flock.agent("flaky_service").circuit_breaker(
    failure_threshold=5,
    recovery_timeout=timedelta(minutes=1),
    half_open_attempts=3
)
```

**Why this matters:**
- Transient failures don't kill workflows
- Poison messages don't block processing
- Cascading failures auto-recover
- Full observability of failure modes

**Target:** Mid-February

---

### 🔴 Priority 1: Aggregation Patterns

**Problem:** Common pattern missing from v0.5: "Run N agents, aggregate results."

**Solution:**

```python
# Map-reduce pattern
@flock_type
class NewsDigest(BaseModel):
    summaries: list[str]
    combined_analysis: str

editor = flock.agent("editor").aggregates(
    NewsAnalysis,
    into=NewsDigest,
    strategy=AggregationStrategy.COLLECT_ALL,
    trigger_on_count=8,  # Wait for all 8 category analysts
    timeout=timedelta(seconds=30)
)

# Voting/consensus pattern
moderator = flock.agent("moderator").aggregates(
    ContentReview,
    into=ModerationDecision,
    strategy=AggregationStrategy.MAJORITY_VOTE,
    field="is_safe"
)

# Best-result selection
selector = flock.agent("selector").aggregates(
    TranslationCandidate,
    into=FinalTranslation,
    strategy=AggregationStrategy.MAX_BY,
    score=lambda t: t.quality_score
)
```

**Why this matters:**
- Multi-agent consensus (not single-agent oracle)
- Parallel execution → aggregation (common pattern)
- Voting for content moderation
- Quality-based selection

**Target:** End of February

---

## 🚀 Q2 2025: Enterprise Infrastructure

**Target: End of Q2 2025 (April - June)**

### 🟡 Priority 2: Kafka Event Backbone

**Problem:** Need to decouple producers from consumers. Enable event replay and time-travel debugging.

**Solution:**

```python
# Kafka as persistent event log
flock = Flock(
    "openai/gpt-4.1",
    event_bus=KafkaEventBus(
        brokers=["kafka1:9092", "kafka2:9092"],
        topics={
            "artifacts": "flock.artifacts",
            "events": "flock.events"
        },
        retention=timedelta(days=30)
    )
)

# Event replay for debugging
await flock.replay(
    from_timestamp=datetime(2025, 1, 15, 14, 30),
    to_timestamp=datetime(2025, 1, 15, 14, 45),
    agents=["diagnostician", "prescriber"]
)

# Time-travel debugging
await flock.restore_checkpoint("workflow_123", timestamp=datetime(2025, 1, 15, 14, 35))
```

**Why this matters:**
- Replay production issues in development
- Audit trail for compliance (immutable log)
- Multi-region replication
- Backfill new agents with historical data

**Target:** End of March

---

### 🟡 Priority 2: Kubernetes-Native Deployment

**Problem:** Agents should deploy like microservices.

**Solution:**

```bash
# Helm chart for production deployment
helm install flock-production whiteduck/flock \
  --set persistence.backend=redis \
  --set persistence.redis.url=redis://cluster:6379 \
  --set eventBus.backend=kafka \
  --set eventBus.kafka.brokers=kafka:9092 \
  --set agents.replicas=3 \
  --set dashboard.enabled=true \
  --set dashboard.auth.provider=oauth2

# Agent auto-scaling based on blackboard queue depth
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: flock-agents
spec:
  scaleTargetRef:
    name: flock-agent-pool
  metrics:
  - type: Pods
    pods:
      metric:
        name: blackboard_queue_depth
      target:
        type: AverageValue
        averageValue: "10"
```

**Why this matters:**
- Deploy agents like any other cloud-native app
- Auto-scale based on workload
- Zero-downtime deployments
- Production-grade health checks and readiness probes

**Target:** Mid-April

---

### 🟡 Priority 2: Authentication & Authorization

**Problem:** Multi-tenant SaaS needs real auth.

**Solution:**

```python
# OAuth2/OIDC for dashboard
flock = Flock(
    "openai/gpt-4.1",
    dashboard_auth=OAuthConfig(
        provider="auth0",
        client_id="...",
        tenant_claim="org_id",
        role_claim="role"
    )
)

# API key authentication for API
await flock.serve(
    dashboard=True,
    api_auth=APIKeyAuth(
        keys_backend=PostgresKeyStore(connection="postgresql://...")
    )
)

# Agent-level RBAC (already have labels, add enforcement)
@flock.require_role("admin")
async def dangerous_operation():
    await flock.publish(SystemCommand(...))
```

**Why this matters:**
- Multi-tenant SaaS (customer isolation)
- Role-based access control
- Compliance (SOC2, HIPAA)
- API security

**Target:** End of April

---

## 🚀 Q3 2025: Advanced Orchestration

**Target: End of Q3 2025 (July - September)**

### 🟢 Priority 3: Human-in-the-Loop Approval Patterns

**Problem:** Some workflows need human oversight.

**Solution:**

```python
# Human approval gates
@flock_type
class ApprovalRequest(BaseModel):
    artifact: TradeOrder
    requires_approval: bool = True

trader = flock.agent("trader").publishes(
    TradeOrder,
    approval=ApprovalSpec(
        timeout=timedelta(minutes=5),
        approvers=["risk_manager", "compliance_officer"],
        quorum=2,  # Both must approve
        on_timeout=ApprovalTimeout.REJECT
    )
)

# Dashboard shows pending approvals
# Slack/email notifications
# Audit trail of approval decisions
```

**Why this matters:**
- High-value transactions need human oversight
- Compliance requirements
- Training mode (review before automation)

**Target:** End of July

---

### 🟢 Priority 3: Fan-Out / Fan-In Patterns

**Problem:** Distribute work, collect results.

**Solution:**

```python
# Dynamic fan-out based on runtime data
@flock_type
class DatasetChunk(BaseModel):
    chunk_id: int
    data: list[dict]

splitter = flock.agent("splitter").consumes(LargeDataset).fan_out(
    into=DatasetChunk,
    strategy=FanOutStrategy.BY_SIZE,
    chunk_size=1000
)

# Fan-in with aggregation
collector = flock.agent("collector").fan_in(
    from_type=ProcessedChunk,
    into=FinalResult,
    strategy=FanInStrategy.MERGE,
    merge_fn=lambda chunks: combine(chunks)
)
```

**Why this matters:**
- Parallel processing of large datasets
- Map-reduce over LLM operations
- Sharding for scale

**Target:** End of August

---

### 🟢 Priority 3: Time-Based Scheduling

**Problem:** Not all workflows are event-driven.

**Solution:**

```python
# Cron-like scheduling
flock.schedule(
    agent="daily_reporter",
    trigger=CronTrigger("0 9 * * *"),  # Daily at 9 AM
    publish=ReportRequest(report_type="daily_summary")
)

# Sliding window triggers
flock.schedule(
    agent="trend_analyzer",
    trigger=SlidingWindowTrigger(
        window=timedelta(hours=1),
        slide=timedelta(minutes=15)
    ),
    publish=AnalysisRequest(...)
)

# Event-driven + time-based hybrid
flock.agent("stale_checker").consumes(
    Document,
    where=lambda d: (datetime.now() - d.created_at) > timedelta(days=30)
).publishes(StaleDocumentAlert)
```

**Why this matters:**
- Periodic reporting
- Batch processing windows
- SLA monitoring
- Data freshness checks

**Target:** End of September

---

## 🔮 Beyond 1.0: The Vision

**Post-1.0 ideas (not committed):**

### Multi-Region Deployment
- Active-active blackboard replication
- Geo-distributed agent pools
- Cross-region consistency

### Advanced Optimization
- LLM-powered agent selection ("Which agent should handle this?")
- Automatic prompt optimization (DSPy-based)
- Cost optimization (cheapest agent that meets SLA)

### Migration Tools
- Auto-convert from LangGraph (AST-based)
- CrewAI migration assistant
- AutoGen compatibility layer

### Developer Experience
- VS Code extension (visual blackboard debugger)
- Template marketplace
- Interactive tutorials in dashboard

---

## 📊 Production Readiness Timeline

| Feature | v0.5.0 (Now) | v1.0 (Q3 2025) |
|---------|--------------|----------------|
| **Core Orchestration** | ✅ Complete | ✅ Complete |
| **Type Safety** | ✅ Complete | ✅ Complete |
| **Security Model** | ✅ 5 visibility types | ✅ + OAuth/RBAC |
| **Persistence** | ⚠️ In-memory only | ✅ Redis/Postgres |
| **Event Replay** | ❌ Not available | ✅ Kafka-based |
| **Retry Logic** | ⚠️ Basic | ✅ Advanced |
| **Aggregation** | ❌ Manual | ✅ Built-in patterns |
| **Kubernetes** | ⚠️ Manual YAML | ✅ Helm chart + HPA |
| **Authentication** | ❌ No auth | ✅ OAuth/API keys |
| **Human-in-Loop** | ❌ DIY | ✅ Built-in |
| **Time-Based Jobs** | ❌ DIY | ✅ Built-in |
| **Test Coverage** | ✅ 743 tests, 77% | ✅ 1000+ tests, 85% |

---

## 🎯 Release Criteria for v1.0

**v1.0 will ship when:**

1. ✅ **Production Persistence** - Redis + Postgres backends stable
2. ✅ **Advanced Error Handling** - Retry, circuit breakers, DLQ working
3. ✅ **Aggregation Patterns** - Map-reduce, voting, consensus implemented
4. ✅ **Kubernetes Native** - Helm chart with auto-scaling
5. ✅ **Authentication** - OAuth/OIDC + API key auth
6. ✅ **Event Replay** - Kafka integration with time-travel debugging
7. ✅ **85%+ Test Coverage** - 1000+ tests passing
8. ✅ **Production Validation** - At least 3 companies running in production

**Target Date:** September 30, 2025

---

## 📞 Questions or Feedback?

This roadmap is informed by:
- User feedback and feature requests
- Production deployment learnings
- Enterprise customer requirements
- Industry best practices

**Want to influence the roadmap?**
- Open a GitHub discussion for feature requests
- Share your production use case
- Join our community calls (monthly)
- Email: support@whiteduck.de

---

**Last Updated:** October 8, 2025
**Version:** Roadmap v1.0
**Status:** Commitments through Q3 2025

---

**"We're not building a toy framework. We're building enterprise infrastructure for AI agents."**
