Roadmap

Roadmap

Development plan for Agents.KT, organized into four phases through Q4 2026.

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Phase 1 -- Core DSL (Current)

The foundation: typed agents, skills, knowledge, all six composition operators, guided generation, and the agentic execution loop.

• Agent<IN, OUT> with SRP enforcement
• Agent.prompt -- base context string for the LLM
• Skills-only execution -- all agents run through skills { implementedBy { } }
• Skill.description (mandatory) -- sells the skill to the LLM alongside its type signature
• Skill.knowledge("key", "description") { } -- named lazy context providers; loadFile() inside lambdas
• Skill.toLlmDescription() -- auto-generated markdown (name, types, description, knowledge index); llmDescription("...") override
• Skill.toLlmContext() -- full context: description markdown + all knowledge content
• Skill.knowledgeTools() / KnowledgeTool(name, description, call) -- tools model with lazy per-entry loading
• then -- sequential pipeline with composed execution (no runtime casts)
• / -- parallel fan-out with coroutine concurrency
• * -- forum (multi-agent discussion)
• Single-placement enforcement across all structure types
• .loop {} -- iterative execution with (OUT) -> IN? feedback block
• .branch {} -- conditional routing on sealed types, composable with then
• @Generable("desc") / @Guide / @LlmDescription -- runtime reflection: toLlmDescription(), jsonSchema(), promptFragment(), fromLlmOutput<T>(), PartiallyGenerated<T>
• model { } -- Ollama backend; host, port, temperature; injectable ModelClient for tests
• Agentic execution loop -- multi-turn tool calling with budget controls (maxTurns) + onToolUse observability hook
• Skill selection -- predicate-based skillSelection {} + automatic LLM routing when multiple skills match
• Agent memory -- MemoryBank, memory_read/memory_write/memory_search auto-injected tools
• >> -- security/education wrap

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Phase 2 -- Runtime + Distribution (Q2 2026)

MCP integration, permissions, compile-time annotation processing, native distribution, and session management.

Priority

• Tool<IN, OUT> hierarchy + McpTool<IN, OUT> -- MCP as native Tool inheritance, not a wrapper
• MCP client integration -- McpTool instances consumable alongside local tools
• grants { tools(...) } -- Layer 2 permissions use actual Tool<*,*> references
• Permission model: 3 states -- Granted (auto-runs), Confirmed (user approval), Absent (unavailable)
• KSP annotation processor -- compile-time @Generable; constrained decoding (Ollama) + guided JSON mode (Anthropic/OpenAI)
• Native CLI binary (GraalVM -- no JRE required); brew, npm, pip, curl, apt
• jlink minimal JRE bundle for runtime (~35MB)

Secondary

• Session model -- multi-turn AgentSession, automatic compaction (SUMMARIZE, SLIDING_WINDOW, CUSTOM)
• Reactive context hooks -- beforeInference, afterToolCall, onBudgetThreshold
• .spawn {} -- independent sub-agent lifecycle, AgentHandle<OUT>, parent-managed join
• Pipeline observability -- observe {} event handler, Flow<PipelineEvent> for streaming UIs
• Serialization -- agent.json, A2A AgentCard
• JAR bundles and folder-based assembly
• Gradle plugin

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Phase 3 -- Production (Q3 2026)

Full production readiness: advanced structure DSL, testing framework, protocol support, and observability.

• Layer 2: Full Structure DSL with delegates, grants, authority, routing, escalation
• All 37 compile-time validations enforced by Gradle plugin
• AgentUnit testing framework -- unit, semantic (LLM-as-judge), Skill Coverage metrics
• A2A protocol support (server + client)
• File-based knowledge: skill.md, reference, examples, checklist + RAG pipeline
• Production observability: OpenTelemetry traces
• Team DSL -- swarm coordination (if isolated execution available)

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Phase 4 -- Ecosystem (Q4 2026)

Developer experience, marketplace, and tooling.

• Knowledge packs -- battle-tested prompt libraries for common domains
• Agent generation from natural language (NL -> Kotlin DSL)
• Skillify -- extract reusable skills from session transcripts
• Visual structure editor, UML bidirectional conversion
• Knowledge marketplace

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Timeline Overview

2026
  Q1  ████████████████  Phase 1: Core DSL (current)
  Q2  ████████████████  Phase 2: Runtime + Distribution
  Q3  ████████████████  Phase 3: Production
  Q4  ████████████████  Phase 4: Ecosystem

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What You Can Build Today (Phase 1)

Capability	Status
Typed agents with Agent<IN, OUT>	Available
Pure Kotlin skills (implementedBy)	Available
LLM-driven skills (tools())	Available
Knowledge (lazy and eager)	Available
Sequential pipelines (then)	Available
Parallel fan-out (/)	Available
Forum deliberation (*)	Available
Iterative loops (.loop {})	Available
Conditional branching (.branch {})	Available
Ollama integration	Available
Mock testing with ModelClient	Available
Guided generation (@Generable, @Guide)	Available
Lenient JSON parsing (fromLlmOutput)	Available
Partial generation (PartiallyGenerated)	Available
Agent memory (MemoryBank)	Available
Skill selection (predicate + LLM)	Available
Budget controls (maxTurns)	Available
Observability hooks	Available
Tool error types (ToolError)	Available

What is Coming Next (Phase 2)

Capability	ETA
MCP tool integration	Q2 2026
Permission model	Q2 2026
KSP compile-time generation	Q2 2026
Native CLI (no JRE)	Q2 2026
Session management	Q2 2026
Pipeline observability	Q2 2026

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See also: Architecture Overview | API Quick Reference | Installation and Setup