AILANG Roadmap
This page is automatically generated from the design_docs/planned directory. Each item represents a planned feature or improvement with a detailed design document.
For completed features, see Design Documents.
Planned for v1.1.0
- M-EXECUTOR-VARIANTS Sprint Plan
- M-EXECUTOR-VARIANTS: Per-Agent Docker Image Variants
- M-PROCESS-MODES: Replay-Contract Modes for the Process Effect
Planned for v1.0.0
- Global Collaboration Hub - Cross-Computer Agent Collaboration
- M-AGENT: std/agent Module for AI Agent Orchestration
- M-CSP-SESSION-TYPES: CSP Concurrency with Session Types
- M-DX-PACKAGE-DOGFOODING: DX Issues Found Building AILANG Packages in AILANG
- M-EFFECT-REFINEMENT: Parameterised Effects and Unified Replay Contracts
- M-ENTROPY: Semantic Entropy Budgets
- M-EU-COMPLIANCE-EFFECTS: Regulatory Trigger Effects for Agentic Systems
- M-FORALL-PROPERTIES: Direct-Core Evaluation for
properties [forall(...) => ...]Blocks - M-PERF4: Bytecode Interpreter (Stretch Goal)
- M-QUASI: Typed Quasiquotes (String Templates)
- Sprint Plan: M-TYPE-V2-MIGRATION
- M-TYPE-V2-MIGRATION: Complete TFunc → TFunc2 Type System Migration
Planned for v0.25.0
- M-MOTOKO-AGENT-SYSTEM-PROMPT — give motoko a lean agentic system prompt
- M-MOTOKO-FAILURE-MODE-OBSERVABILITY — segment agent failures by mode (disengage vs grind-wrong)
- M-OLLAMA-PER-MODEL-MAX-TOKENS — flow the registry's declared max_output_tokens to motoko
Planned for v0.24.0
- M-AILANG-ERROR-QUALITY: Error Messages as the Lever for Small-Model Success
- Sprint Plan — M-EVAL-BENCHMARK-UI-CONSOLIDATION
- Sprint Plan — M-EVAL-ELO-PERSIST (M-EVAL-RATING-EFFICIENCY part 2)
- M-EVAL-FINETUNING-DATA-PIPELINE: Capture-to-QLoRA on the Local Rig
- M-EVAL-LOCAL-OLLAMA: Production-Grade Local Ollama Eval Rig
- M-EVAL-OPENROUTER-BASELINE-ROTATION: Cloud Baseline Three Candidate Models
- M-EVAL-RATING-EFFICIENCY: ELO-Style Benchmark Difficulty + Targeted Reruns + Tier Saturation
- M-EVAL-REGRESSION-DETECTOR-CONTRACT: Specify trial grouping, flaky-vs-persistent classification, and infra-noise policy
- M-EVAL-STREAM-HEALTH-RETRY: Mid-stream death detection + fast retry for the streaming executor
- M-MOTOKO-OLLAMA-LOOP-CONVERGENCE: Make the Motoko Agent Loop Terminate Against Local Ollama
- M-MSG-AUTO-TRIAGE-PIPELINE: Autonomous Inbound Triage + Central Notification Bus
- Sprint Plan: M-MSG-TRIAGE-ROUTER (Build Order Step 1)
- M-NOTIFY-CHANNELS-FRAMEWORK: Outbound Notification Channels (Adapter Framework)
- Sprint Plan: M-NOTIFY-CHANNELS (Build Order Step 2)
- M-PRELUDE-OPTION-RESULT: Add Option/Result to the prelude (structural AI-DX fix)
- M-PROMPT-RUN-LENGTH-PATTERNS: Concise Recursive Solutions to Prevent Token-Limit Truncation
- M-PROMPT-LOG-FILE-ANALYZER: String ops pipeline example (split→filter→map→join)
- Prompt Gap: Match Expression Guard Syntax
- M-PROMPT-NESTED-ADT-PATTERNS: Clarify Nested ADT Match Support and Prevent Over-Verbose Balance Functions
- Prompt Gap: Option/None Requires Explicit Import
- M-PROMPT-SINGLE-FILE-MODULE: Teach Single-File Module Convention for Benchmarks
- Prompt Gap: Split Returns List, Not String
- M-PROMPT-STRING-CONCAT-PLUSPLUS: The
++string-concat reflex — LARGELY SOLVED, monitor only - M-SYNTAX-AI-FORGIVING: Forgiving statement syntax — accept the AI's newline-separator prior
- M-TYPE-CONSTRAINTS: Type parameter constraints (
[a: Ord]) — explicit-comparator workaround - Sprint Plan: M-TYPE-LIST-ELEMENT-SOUNDNESS
- M-TYPE-LIST-ELEMENT-SOUNDNESS: List-Literal Element Types Escape the Type Checker
Planned for v0.23.0
- EVAL CANDIDATE —
dense_operator_programpersistent failure analysis - M-BYTES-TOINTS-BYTEAT: Add byte-to-int extraction primitives to std/bytes
- M-EVAL-METRICS-TAXONOMY: Beyond Pass/Fail — Metrics for Continuous LLM Eval
- M-EVAL-OS-LONGITUDINAL Phases 2–5 — Sprint Plan
- M-EVAL-OS-LONGITUDINAL — Longitudinal OS-model eval as language-design feedback
- M-EVAL-SLIM-PROMPT-SELF-DISCOVERY
- M-HARNESS-DSL: AILANG as Self-Hosting Coordinator Specification Language
- M-HARNESS-STATE: Shared Harness Substrate with Belief-State Synchronization
- M-ORACLE-ADEQUACY: Convergence Oracles and Evidence Bundles for the Eval Harness
- M-PERMISSION-MODEL: Typed Permission Tiers for the Coordinator via Effect Rows
- M-PROGRAMBENCH-GAP-PROBE: Probe AILANG against ProgramBench-style "rebuild a CLI from spec" tasks
- M-PROMPT-STDLIB-COVERAGE
- M-TRACE-FEEDBACK: Execution-Trace Feedback Loop for Harness Diagnostics
- M-VERA-BENCH-INTEGRATION: Add AILANG as a target language in VeraBench
- M-VERA-BENCH-INTEGRATION Sprint Plan
Planned for v0.22.0
- M-COG-MEMORY: Semantic Memory —
!: SharedMem+!: SemanticSearch - M-COG-MESH: Distributed Cognitive Mesh — Collaborative Demo + Distributed Transports
- M-EFFECT-ROW-POLY-PARAMS — Effect-row polymorphism on higher-order arguments
- M-LAMBDA-OPEN-RECORD-PATTERN — Lambda +
{field, ...}pattern doesn't propagate row polymorphism - M-MATCH-XCHECK-ERROR-QUALITY —
MatchForeignConstructorErrorshows empty constructor list for non-imported ADTs - Sprint Plan — M-PATTERN-AND-INVOCATION-REPAIR
- M-TERMINAL-BENCH: Terminal-Bench Cloud Evaluation (AILANG vs Python)
- M-THREE-CAMPS-LANGUAGE-SURVEY: Gap Analysis Across 16 AI-Designed Languages
- M-THREE-CAMPS Sprint Plan (Gap-Analysis-First)
- M-WASM-TYPECHECK-LIMITS — depth-budget guard + clear error for WASM type-checker overflow
- M-ZERO-ARG-INVOCATION-SURFACES — Unify zero-arg export invocation across all surfaces
Planned for v0.21.0
- M-CHECK-STRICT-FALLBACKS — Static detection of "Ok contains default-valued literal" anti-pattern
- Sprint Plan: M-COG-RUNTIME-BROWSER
- M-COG-RUNTIME-BROWSER: Cognitive OS Browser Substrate
- M-EFFECT-HANDLERS: User-Definable Effect Handlers
- M-EVAL-AGENT-PYTHON-STDIO-WIRING — Python agent-mode runner drops
cli_argsandstdinfrom benchmark spec - M-MOTOKO-CLOUD-MIGRATION: Replace
claude -pOAuth Executors with motoko + OpenRouter in Production - M-SERVEAPI-GET-QUERY-SHADOW — GET query args silently shadowed by zero-value padding
- m stdlib html streaming
- M-WASM-REFLECTIVE-RUNTIME: Cognitive OS (Umbrella)
- M-ZERO-LANGUAGE-LEARNINGS: Borrowed Ideas from Vercel Labs' Zero
Planned for v0.19.0
- M-BENCHMARK-DATA-INTEGRITY: Benchmark Dashboard Data Integrity Audit
- M-COORDINATOR-INBOX-WILDCARDS: glob-based inbox routing in the agent registry
- Sprint Plan: M-MOTOKO-EXT-PER-TASK
- M-MOTOKO-EXT-PER-TASK: Per-Invocation motoko Extension Configuration
- M-WASM-AI-STEP-VIA-MESSAGES: WASM
ai.stepmediated by the cloud message bus - M-WASM-CLOUD-MESSAGES: AILANG-WASM browser participates in the cloud message bus
Planned for v0.18.9
Planned for v0.18.0
- Sprint Plan: M-MOTOKO-EXECUTOR-ADAPTER
- M-MOTOKO-EXECUTOR-ADAPTER: motoko_agent as a CLI-Subprocess Executor
- Sprint Plan: M-PKG-AUTO-UPDATE-DX
- M-SERVE-API-LIVE-TOOL-REGISTRY: Live Tool Registry for Agentic Sessions
Planned for v0.17.0
- M-AGENT-LOOP-ARCHITECTURE: Where Should the Multi-Turn Agent Loop Live?
- M-AI-OPENAI-LOCAL-ENDPOINT-RELAX — Allow empty OPENAI_API_KEY when OPENAI_BASE_URL is custom
- M-AI-REASONING-EFFORT — Cross-Provider Request-Side Reasoning Control
- M-AI-STREAMING-HELPER Sprint Plan
- M-AI-STREAMING-HELPER: AI Token Streaming Helper + Cross-Domain Discovery
- M-BENCH-MOTOKO: Motoko Agent as a Benchmark Executor
- M-EXTERNAL-CONSUMER-DX Sprint Plan
- M-EXTERNAL-CONSUMER-DX: Diagnostics & Artifacts for External AILANG Projects
Planned for v0.16.0
- M-AGENT-SAFE-RUNNER: Turnkey Sandboxed Execution for AI-Authored AILANG Programs
- M-AI-EFFECT-MODES-FOLLOWUPS: Close the Loose Ends from v0.15.0 AI Modes
Planned for v0.15.2
Planned for v0.15.0
- M-AI-PROVIDER-CONFIG: Config-Driven AI Providers via Package Manifests
- M-BENCHMARK-SECTION: Multi-Page Benchmark Section for AILANG Website
- M-CASCADE-OBSERVABILITY: Surface Cascade AI Conversation History
- M-EVAL-GAP-FIXES: Eval-discovered AILANG and harness improvements
- M-EVAL-RESULTS-FOLDER-STRUCTURE: Model-First Eval Results Layout
- M-EVAL-TRUST-SIGNALS: External Trust Signals for AILANG Eval Results
- M-FEEDBACK-TRIAGE-GATE: Cost & abuse gate for the public feedback → agent pipeline
- Sprint Plan: M-PKG-FEEDBACK-LOOP (Test + activate per-package feedback + macOS notifier)
- M-PKG-FEEDBACK-LOOP: Validate the per-package feedback loop end-to-end
Planned for v0.13.0
- AILANG: Auto-Caps and Capability Inference
- M-REPL1: REPL Persistent Type Bindings & Module Loading
- M-TOOLING: Deterministic CLI for AI Agents (v0.3.15)
- M-ARCH-BOUNDARIES: Formalize Dashboard/Core Separation
- M-ARCH1: AI Provider Base Class
- M-ARCH2: Daemon God Object Refactor
- M-ARCH3: Task Classification Consolidation
- M-ARCH4: Executor Stream Processor
- M-ARCH5: Error Handling Strategy
- M-BUG-LETREC-SINGLE-CALL: Letrec with Single Recursive Call Fails
- M-BYTECODE-VM-PARITY-BUGS — Remaining VM/Eval Divergences
- M-CALL-SUGAR: Optional Parenthesized Call Syntax
- M-CLOUD-EVAL: Distributed Cloud Evaluation Workers
- M-CLOUD-OBSERVATORY: Observatory Firestore Backend for Cloud Run
- M-CONCURRENCY-LEVERAGE: Leverage Fork() Across All Execution Modes
- Sprint Plan: M-COORD-CODEX (Codex Executor for Coordinator + Evals)
- M-COORD-CODEX: Codex Executor for Coordinator + Evals
- M-COORD-THINKING: Extended Thinking Levels for Coordinator Skills
- M-COPILOT-CLI: GitHub Copilot CLI Integration
- M-CRYPTORAND: Cryptographic Randomness as a First-Class Effect
- M-D4: Design-Doc-Driven Development (D4)
- M-DASHBOARD-PUBSUB-EVENTS: Dashboard Pub/Sub Event Subscriber
- Dashboard Simplification - Remaining Work (v0.7.0)
- M-DX-AGENT-EVAL-GAPS: Agent DX Improvements from v0.9.0 Eval Analysis
- M-DX-AI-DISCOVERY: Improve AI Agent Stdlib Discovery
- M-DX-EXAMPLES-COVERAGE: Stdlib Examples for Every Module
- M-DX-EXPECTED-FAIL-FIXES: Fix Remaining Expected-Fail Examples
- M-DX-JSON-BOOL: JSON Boolean Coercion and Firestore Encoding Consistency
- M-DX-RECORD-CONS: Record Literal + :: Cons Pattern Bug
- M-DX-SPLIT-ARG: Compile-Time Warning for Reversed
splitArguments - M-DX-TAPP-TRECORD: Type Inference Bug with Nested [[RecordType]]
- Inline Tests Documentation & Examples (M-DX23)
- M-DX26: Property Test "Empty Program" Bug
- M-DX27: GitHub Repository Search Fallback for
ailang docs search - M-ERROR-PROP: Error Propagation Operator (
?) - Sprint Plan: M-EVAL-BOUNDED-PIPELINE
- M-EVAL-BOUNDED-PIPELINE: Fused Bounded Combinators + Memory Ceiling
- M-EVAL-EXPAND: Expanding the AILANG Eval Bench (Harnesses, Languages, Open-Source Models)
- M-EVAL-LAZY-PIPELINE: Lazy List Pipelines to Fix OOM on Large Documents
- M-EXEC-HIERARCHY-REFACTOR: Executive Hierarchy Graph Visualization
- Sprint Plan: M-FILE-HANDLING
- M-FILE-HANDLING: fileData/fileUri Support & serve-api POST Param Fix
- M-GEMINI-INTERACTIONS: Google Gemini Interactions API Support
- Sprint Plan: M-GENERIC-PIPELINE
- Bug:
show()builtin fails in serve-api when transitive deps also useshow() - M-LOCOBENCH: Long-Context Benchmark Integration
- Improve Module Loading Error Messages for Standalone/Scratch Files
- Sprint Plan: M-OBS-CONFIGURABLE-SPAN-FILTERING
- M-OBS-CONFIGURABLE-SPAN-FILTERING: Configurable Observatory Span Filtering
- M-PIPE-OPERATOR: Pipe operator and Option chaining
- M-PKG-TRUSTED-AUTONOMOUS-EVOLUTION: Secure Autonomous Package Evolution
- Sprint Plan: M-PROTOCOL-SUPPORT
- M-PROVENANCE: Code Provenance Tracing & Agent Trace Export
- M-REFLECT: Structural Reflection & User-Defined Type Classes
- M-TASK-GRAPH-SPANS-UNIFICATION: Fix TaskHierarchyGraph Filtering
- M-TYPEENV-SUB: TypeEnv Substitution Gap — ADT Return Types Lost in Cross-Module Exports
- M-UI-REFACTOR: Refactor UI Folder for AI-Friendly File Sizes
- Sprint Plan: M-UI-REFACTOR - AI-Friendly File Sizes
- Sit-Rep: Determinism vs. Unpredictability — The Rand Axiom Tension
Planned for docparse-billing
- DX Improvements Discovered During Billing Package Creation
- M-BILLING: DocParse Billing and Agent-Assisted Payment
- M-BILLING: DocParse Responsibility
- M-BILLING: ailang-multivac Responsibility
- M-BILLING: ailang-packages Responsibility
Long-term Vision
AILANG is designed as a deterministic language for autonomous AI code synthesis. The long-term roadmap includes:
- Structural Reflection - Typed quasiquotes and AST manipulation
- Schema Registry - Machine-readable type and effect definitions
- Capability Budgets - Resource-bounded effects
- Training Data Export - Execution traces for AI self-training
For the complete vision, see Why AILANG and Vision.
Generated at build time. 176 planned features across 16 upcoming versions.