# Autonomous AI Agents (typdialog-ag)\n\n**Status**: 🔴 Planned (Q2 2025 target)\n\nAutonomous AI Agents is a planned feature that enables AI agents to execute multi-step\ninfrastructure provisioning workflows with minimal human intervention. Agents make\ndecisions, adapt to changing conditions, and execute complex tasks while maintaining\nsecurity and requiring human approval for critical operations.\n\n## Feature Overview\n\n### What It Does\n\nEnable AI agents to manage complex provisioning workflows:\n\n```\nUser Goal:\n  "Set up a complete development environment with:\n   - PostgreSQL database\n   - Redis cache\n   - Kubernetes cluster\n   - Monitoring stack\n   - Logging infrastructure"\n\nAI Agent executes:\n1. Analyzes requirements and constraints\n2. Plans multi-step deployment sequence\n3. Creates configurations for all components\n4. Validates configurations against policies\n5. Requests human approval for critical decisions\n6. Executes deployment in correct order\n7. Monitors for failures and adapts\n8. Reports completion and recommendations\n```\n\n## Agent Capabilities\n\n### Multi-Step Workflow Execution\n\nAgents coordinate complex, multi-component deployments:\n\n```\nGoal: "Deploy production Kubernetes cluster with managed databases"\n\nAgent Plan:\n  Phase 1: Infrastructure\n    ├─ Create VPC and networking\n    ├─ Set up security groups\n    └─ Configure IAM roles\n\n  Phase 2: Kubernetes\n    ├─ Create EKS cluster\n    ├─ Configure network plugins\n    ├─ Set up autoscaling\n    └─ Install cluster add-ons\n\n  Phase 3: Managed Services\n    ├─ Provision RDS PostgreSQL\n    ├─ Configure backups\n    └─ Set up replicas\n\n  Phase 4: Observability\n    ├─ Deploy Prometheus\n    ├─ Deploy Grafana\n    ├─ Configure log collection\n    └─ Set up alerting\n\n  Phase 5: Validation\n    ├─ Run smoke tests\n    ├─ Verify connectivity\n    └─ Check compliance\n```\n\n### Adaptive Decision Making\n\nAgents adapt to conditions and make intelligent decisions:\n\n```\nScenario: Database provisioning fails due to resource quota\n\nStandard approach (human):\n1. Detect failure\n2. Investigate issue\n3. Decide on fix (reduce size, change region, etc.)\n4. Update config\n5. Retry\n\nAgent approach:\n1. Detect failure\n2. Analyze error: "Quota exceeded for db.r6g.xlarge"\n3. Check available options:\n   - Try smaller instance: db.r6g.large (may be insufficient)\n   - Try different region: different cost, latency\n   - Request quota increase (requires human approval)\n4. Ask human: "Quota exceeded. Suggest: use db.r6g.large instead \n   (slightly reduced performance). Approve? [yes/no/try-other]"\n5. Execute based on approval\n6. Continue workflow\n```\n\n### Dependency Management\n\nAgents understand resource dependencies:\n\n```\nKnowledge graph of dependencies:\n\n  VPC ──→ Subnets ──→ EC2 Instances\n   ├─────────→ Security Groups\n   └────→ NAT Gateway ──→ Route Tables\n\n  RDS ──→ DB Subnet Group ──→ VPC\n   ├─────────→ Security Group\n   └────→ Parameter Group\n\nAgent ensures:\n- VPC exists before creating subnets\n- Subnets exist before creating EC2\n- Security groups reference correct VPC\n- Deployment order respects all dependencies\n- Rollback order is reverse of creation\n```\n\n## Architecture\n\n### Agent Design Pattern\n\n```\n┌────────────────────────────────────────────────────────┐\n│ Agent Supervisor (Orchestrator)                        │\n│ - Accepts user goal                                    │\n│ - Plans workflow                                       │\n│ - Coordinates specialist agents                        │\n│ - Requests human approvals                             │\n│ - Monitors overall progress                            │\n└────────────────────────────────────────────────────────┘\n        ↑                    ↑                    ↑\n        │                    │                    │\n        ↓                    ↓                    ↓\n┌──────────────┐  ┌──────────────┐  ┌──────────────┐\n│ Database     │  │ Kubernetes   │  │ Monitoring   │\n│ Specialist   │  │ Specialist   │  │ Specialist   │\n│              │  │              │  │              │\n│ Tasks:       │  │ Tasks:       │  │ Tasks:       │\n│ - Create DB  │  │ - Create K8s │  │ - Deploy     │\n│ - Configure  │  │ - Configure  │  │   Prometheus │\n│ - Validate   │  │ - Validate   │  │ - Deploy     │\n│ - Report     │  │ - Report     │  │   Grafana    │\n└──────────────┘  └──────────────┘  └──────────────┘\n```\n\n### Agent Workflow\n\n```\nStart: User Goal\n  ↓\n┌─────────────────────────────────────────┐\n│ Goal Analysis & Planning                │\n│ - Parse user intent                     │\n│ - Identify resources needed             │\n│ - Plan dependency graph                 │\n│ - Generate task list                    │\n└──────────────┬──────────────────────────┘\n               ↓\n┌─────────────────────────────────────────┐\n│ Resource Generation                     │\n│ - Generate configs for each resource    │\n│ - Validate against schemas              │\n│ - Check compliance policies             │\n│ - Identify potential issues             │\n└──────────────┬──────────────────────────┘\n               ↓\n         Human Review Point?\n         ├─ No issues: Continue\n         └─ Issues found: Request approval/modification\n               ↓\n┌─────────────────────────────────────────┐\n│ Execution Plan Verification             │\n│ - Check all configs are valid           │\n│ - Verify dependencies are resolvable    │\n│ - Estimate costs and timeline           │\n│ - Identify risks                        │\n└──────────────┬──────────────────────────┘\n               ↓\n         Execute Workflow?\n         ├─ User approves: Start execution\n         └─ User modifies: Return to planning\n               ↓\n┌─────────────────────────────────────────┐\n│ Phase-by-Phase Execution                │\n│ - Execute one logical phase             │\n│ - Monitor for errors                    │\n│ - Report progress                       │\n│ - Ask for decisions if needed           │\n└──────────────┬──────────────────────────┘\n               ↓\n         All Phases Complete?\n         ├─ No: Continue to next phase\n         └─ Yes: Final validation\n               ↓\n┌─────────────────────────────────────────┐\n│ Final Validation & Reporting            │\n│ - Smoke tests                           │\n│ - Connectivity tests                    │\n│ - Compliance verification               │\n│ - Performance checks                    │\n│ - Generate final report                 │\n└──────────────┬──────────────────────────┘\n               ↓\nSuccess: Deployment Complete\n```\n\n## Planned Agent Types\n\n### 1. Database Specialist Agent\n\n```\nResponsibilities:\n- Create and configure databases\n- Set up replication and backups\n- Configure encryption and security\n- Monitor database health\n- Handle database-specific issues\n\nExamples:\n- Provision PostgreSQL cluster with replication\n- Set up MySQL with read replicas\n- Configure MongoDB sharding\n- Create backup pipelines\n```\n\n### 2. Kubernetes Specialist Agent\n\n```\nResponsibilities:\n- Create and configure Kubernetes clusters\n- Configure networking and ingress\n- Set up autoscaling policies\n- Deploy cluster add-ons\n- Manage workload placement\n\nExamples:\n- Create EKS/GKE/AKS cluster\n- Configure Istio service mesh\n- Deploy Prometheus + Grafana\n- Configure auto-scaling policies\n```\n\n### 3. Infrastructure Agent\n\n```\nResponsibilities:\n- Create networking infrastructure\n- Configure security and firewalls\n- Set up load balancers\n- Configure DNS and CDN\n- Manage identity and access\n\nExamples:\n- Create VPC with subnets\n- Configure security groups\n- Set up application load balancer\n- Configure Route53 DNS\n```\n\n### 4. Monitoring Agent\n\n```\nResponsibilities:\n- Deploy monitoring stack\n- Configure alerting\n- Set up logging infrastructure\n- Create dashboards\n- Configure notification channels\n\nExamples:\n- Deploy Prometheus + Grafana\n- Set up CloudWatch dashboards\n- Configure log aggregation\n- Set up PagerDuty integration\n```\n\n### 5. Compliance Agent\n\n```\nResponsibilities:\n- Check security policies\n- Verify compliance requirements\n- Audit configurations\n- Generate compliance reports\n- Recommend security improvements\n\nExamples:\n- Check PCI-DSS compliance\n- Verify encryption settings\n- Audit access controls\n- Generate compliance report\n```\n\n## Usage Examples\n\n### Example 1: Development Environment Setup\n\n```\n$ provisioning ai agent --goal "Set up dev environment for Python web app"\n\nAgent Plan Generated:\n┌─────────────────────────────────────────┐\n│ Environment: Development                │\n│ Components: PostgreSQL + Redis + Monitoring\n│                                         │\n│ Phase 1: Database (1-2 min)            │\n│   - PostgreSQL 15                       │\n│   - 10 GB storage                       │\n│   - Dev security settings               │\n│                                         │\n│ Phase 2: Cache (1 min)                 │\n│   - Redis Cluster Mode disabled         │\n│   - Single node                         │\n│   - 2 GB memory                         │\n│                                         │\n│ Phase 3: Monitoring (1-2 min)          │\n│   - Prometheus (metrics)                │\n│   - Grafana (dashboards)                │\n│   - Log aggregation                     │\n│                                         │\n│ Estimated time: 5-10 minutes            │\n│ Estimated cost: $15/month               │\n│                                         │\n│ [Approve] [Modify] [Cancel]             │\n└─────────────────────────────────────────┘\n\nAgent: Approve to proceed with setup.\n\nUser: Approve\n\n[Agent execution starts]\nCreating PostgreSQL...     [████████░░] 80%\nCreating Redis...          [░░░░░░░░░░] 0%\n[Waiting for PostgreSQL creation...]\n\nPostgreSQL created successfully!\nConnection string: postgresql://dev:pwd@db.internal:5432/app\n\nCreating Redis...          [████████░░] 80%\n[Waiting for Redis creation...]\n\nRedis created successfully!\nConnection string: redis://cache.internal:6379\n\nDeploying monitoring...    [████████░░] 80%\n[Waiting for Grafana startup...]\n\nAll services deployed successfully!\nGrafana dashboards: [http://grafana.internal:3000](http://grafana.internal:3000)\n```\n\n### Example 2: Production Kubernetes Deployment\n\n```\n$ provisioning ai agent --interactive \\n    --goal "Deploy production Kubernetes cluster with managed databases"\n\nAgent Analysis:\n- Cluster size: 3-10 nodes (auto-scaling)\n- Databases: RDS PostgreSQL + ElastiCache Redis\n- Monitoring: Full observability stack\n- Security: TLS, encryption, VPC isolation\n\nAgent suggests modifications:\n  1. Enable cross-AZ deployment for HA\n  2. Add backup retention: 30 days\n  3. Add network policies for security\n  4. Enable cluster autoscaling\n  Approve all? [yes/review]\n\nUser: Review\n\nAgent points out:\n  - Network policies may affect performance\n  - Cross-AZ increases costs by ~20%\n  - Backup retention meets compliance\n\nUser: Approve with modifications\n  - Network policies: use audit mode first\n  - Keep cross-AZ\n  - Keep backups\n\n[Agent creates configs with modifications]\n\nConfigs generated:\n  ✓ infrastructure/vpc.ncl\n  ✓ infrastructure/kubernetes.ncl\n  ✓ databases/postgres.ncl\n  ✓ databases/redis.ncl\n  ✓ monitoring/prometheus.ncl\n  ✓ monitoring/grafana.ncl\n\nEstimated deployment time: 15-20 minutes\nEstimated cost: $2,500/month\n\n[Start deployment?] [Review configs]\n\nUser: Review configs\n\n[User reviews and approves]\n\n[Agent executes deployment in phases]\n```\n\n## Safety and Control\n\n### Human-in-the-Loop Checkpoints\n\nAgents stop and ask humans for approval at critical points:\n\n```\nAutomatic Approval (Agent decides):\n- Create configuration\n- Validate configuration\n- Check dependencies\n- Generate execution plan\n\nHuman Approval Required:\n- First-time resource creation\n- Cost changes > 10%\n- Security policy changes\n- Cross-region deployment\n- Data deletion operations\n- Major version upgrades\n```\n\n### Decision Logging\n\nAll decisions logged for audit trail:\n\n```\nAgent Decision Log:\n| 2025-01-13 10:00:00 | Generate database config |\n| 2025-01-13 10:00:05 | Config validation: PASS |\n| 2025-01-13 10:00:07 | Requesting human approval: "Create new PostgreSQL instance" |\n| 2025-01-13 10:00:45 | Human approval: APPROVED |\n| 2025-01-13 10:00:47 | Cost estimate: $100/month - within budget |\n| 2025-01-13 10:01:00 | Creating infrastructure... |\n| 2025-01-13 10:02:15 | Database created successfully |\n| 2025-01-13 10:02:16 | Running health checks... |\n| 2025-01-13 10:02:45 | Health check: PASSED |\n```\n\n### Rollback Capability\n\nAgents can rollback on failure:\n\n```\nScenario: Database creation succeeds, but Kubernetes creation fails\n\nAgent behavior:\n1. Detect failure in Kubernetes phase\n2. Try recovery (retry, different configuration)\n3. Recovery fails\n4. Ask human: "Kubernetes creation failed. Rollback database creation? [yes/no]"\n5. If yes: Delete database, clean up, report failure\n6. If no: Keep database, manual cleanup needed\n\nFull rollback capability if entire workflow fails before human approval.\n```\n\n## Configuration\n\n### Agent Settings\n\n```\n# In provisioning/config/ai.toml\n[ai.agents]\nenabled = true\n\n# Agent decision-making\nauto_approve_threshold = 0.95  # Approve if confidence > 95%\nrequire_approval_for = [\n  "first_resource_creation",\n  "cost_change_above_percent",\n  "security_policy_change",\n  "data_deletion",\n]\n\ncost_change_threshold_percent = 10\n\n# Execution control\nmax_parallel_phases = 2\nphase_timeout_minutes = 30\nexecution_log_retention_days = 90\n\n# Safety\ndry_run_mode = false  # Always perform dry run first\nrequire_final_approval = true\nrollback_on_failure = true\n\n# Learning\ntrack_agent_decisions = true\ntrack_success_rate = true\nimprove_from_feedback = true\n```\n\n## Success Criteria (Q2 2025)\n\n- ✅ Agents complete 5 standard workflows without human intervention\n- ✅ Cost estimation accuracy within 5%\n- ✅ Execution time matches or beats manual setup by 30%\n- ✅ Success rate > 95% for tested scenarios\n- ✅ Zero unapproved critical decisions\n- ✅ Full decision audit trail for all operations\n- ✅ Rollback capability tested and verified\n- ✅ User satisfaction > 8/10 in testing\n- ✅ Documentation complete with examples\n- ✅ Integration with form assistance and NLC working\n\n## Related Documentation\n\n- [Architecture](architecture.md) - AI system overview\n- [Natural Language Config](natural-language-config.md) - Config generation\n- [AI-Assisted Forms](ai-assisted-forms.md) - Interactive forms\n- [Configuration](configuration.md) - Setup guide\n- [ADR-015](../architecture/adr/adr-015-ai-integration-architecture.md) - Design decisions\n\n---\n\n**Status**: 🔴 Planned\n**Target Release**: Q2 2025\n**Last Updated**: 2025-01-13\n**Component**: typdialog-ag\n**Architecture**: Complete\n**Implementation**: In Design Phase
