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Roadmap

Last verified: v2.0

Current State: v2.0

MEHO v2.0 represents a comprehensive platform with cross-system diagnostic intelligence, 15 connector types, and a production-ready deployment model.

What's Shipped

MEHO has gone through extensive development across 10 milestones, delivering:

  • Cross-system reasoning -- trace problems across Kubernetes, cloud infrastructure, observability stacks, CI/CD pipelines, and collaboration tools in a single conversation
  • 15 connector types -- Infrastructure (Kubernetes, VMware, Proxmox, GCP), Observability (Prometheus, Loki, Tempo, Alertmanager), CI/CD (ArgoCD, GitHub), Collaboration (Jira, Confluence, Email), and Generic (REST via OpenAPI, SOAP via WSDL)
  • Trust model -- three-tier classification (READ/WRITE/DESTRUCTIVE) with approval workflows and audit trail for every operation
  • Intelligent data pipeline -- JSONFlux engine with Arrow tables, Parquet caching, and DuckDB SQL reduction that keeps LLM context manageable even with large datasets
  • Topology auto-discovery -- automatic entity extraction and cross-system resolution (providerID, IP, hostname matching) that builds a connected infrastructure graph
  • Dual-mode chat -- Ask mode for knowledge base Q&A, Agent mode for cross-system investigation
  • Three-tier knowledge architecture -- global, connector-type, and connector-instance scoping with hybrid search (BM25 + semantic) and Voyage AI reranking
  • Token optimization -- 81% cumulative reduction through observation compression, sliding windows, stateful loop management, and step budgets
  • Security hardening -- Content Security Policy, HSTS, CORS lockdown, memory-only auth tokens, Keycloak OIDC integration
  • Investigation visualization -- hypothesis tracking, citations, breadcrumb navigation, and topology animation showing investigation paths

v2.0 Stabilization

The current stabilization milestone focused on hardening what's built:

  • Repository cleanup -- archived planning artifacts, resolved all ESLint and Ruff warnings, updated pre-commit hooks
  • Testing infrastructure -- layered test conftest, PydanticAI test model integration, verified test suites
  • Bug fixes -- resolved critical and major bugs, implemented AWS/Azure providerID parsing, removed dead code and stale references
  • Documentation -- complete documentation rewrite covering all features, all connectors, and deployment guides

What's Next

Testing Expansion

Expanding automated test coverage beyond the current baseline:

  • End-to-end test suite -- automated browser tests covering all user journeys (currently 5 Playwright specs, expanding to full coverage)
  • CI pipeline -- GitHub Actions with automated test gates, coverage thresholds, and deployment checks
  • LLM evaluation -- automated assessment of agent response quality using LLM-as-judge evaluation pipelines
  • Performance baselines -- regression testing for critical paths (agent response time, data pipeline throughput)

Internal Documentation

Engineering reference material for the development team:

  • Architecture decision records -- extracting and documenting the key decisions made during development
  • Connector development guide -- step-by-step guide for adding new connector types to the platform
  • Agent prompt engineering guide -- best practices for authoring skills and tuning agent behavior

Performance Optimization

Fine-tuning the platform for production workloads:

  • Query optimization -- database query analysis and indexing improvements
  • Caching strategy -- intelligent cache invalidation and pre-warming for frequently accessed data
  • Streaming performance -- SSE connection management and message delivery optimization

New Connector Types

Expanding platform coverage based on customer needs:

  • Additional cloud providers (AWS, Azure native connectors)
  • Database connectors (PostgreSQL, MySQL, MongoDB)
  • APM integrations (Datadog, New Relic, Dynatrace)
  • Container orchestration (Docker Swarm, Nomad)

Each new connector type follows the established pattern: typed connector class, operation definitions with trust classification, topology entity extraction, and auto-generated skills.

Platform Features

Capabilities under consideration for future milestones:

  • Investigation replay -- post-investigation timeline scrubber for reviewing past diagnostic sessions
  • Scheduled reports -- automated periodic investigations with report delivery
  • Team collaboration -- shared investigation sessions with real-time presence
  • Custom dashboards -- saved query templates for recurring diagnostic patterns

Contributing

MEHO's roadmap is driven by real-world operational needs. If you have suggestions for new connector types, features, or improvements, reach out to the evoila engineering team.