This feature is not yet implemented. This page describes the vision and design goals for shared semantic state in AILANG. Implementation is planned for v0.6.0.
Design Document: semantic-caching.md
Shared Semantic State — AILANG's Cognitive Kernel
AILANG is the first language designed for distributed cognition — where agents don't talk to each other, they think together in a shared semantic memory.
This document explains why shared semantic state is not just useful but transformational, and why making it a first-class language primitive puts AILANG in a category no other language can reach.
Why This Is Fundamentally New
Traditional languages assume:
- State is symbolic — strings, numbers, structs
- State is local — per process, per service
- Coordination is explicit — locks, queues, RPCs
- AI is external — models are called, not integrated
So everything is bolted on:
| Infrastructure | Purpose |
|---|---|
| Redis | Shared memory |
| Kafka | Coordination |
| Vector DBs | Embeddings |
| HTTP/RPC | Multi-agent comms |
| LRU caches | Performance |
| Message queues | Agent collaboration |
The result: AI systems are built on a patchwork of leaky abstractions where no component actually understands the others. Caches, models, agents, embeddings, plans, world state — all live in isolated silos.
AILANG Breaks This Paradigm
AILANG treats shared semantic memory as a language-level construct, not an infrastructure accident.
This is the same conceptual leap that:
- Lisp made for symbolic computation
- SQL made for relational data
- CUDA made for massive parallelism
AILANG is doing it for AI-native distributed cognition.
What Shared Semantic State Unlocks
A. Agents Think Together, Not Just Talk
Existing systems rely on message passing:
Agent A → "Here is a plan, in words" → Agent B
This is:
- Slow — tokenization overhead
- Lossy — text collapses latent state
- Brittle — interpretation drift
- Expensive — each hop costs tokens
- Low-bandwidth — natural language bottleneck
With shared semantic state:
Agent A → writes a sem_frame embedding
Agent B → continues from that embedding
This is:
- Zero-token — direct state sharing
- Continuity-preserved — no information loss
- High-bandwidth — full vector space
- Deterministic — CAS ensures consistency
From the LLM's point of view, they share latent assets — planners, critics, executors all manipulate the same semantic object.
This turns a group of AILANG agents into something closer to a distributed Transformer, not a bunch of microservices.
No other language gives you this.
B. Incremental Computation, Not "Repeat Everything"
Normal LLM systems redo work constantly:
- Re-summarize the same document
- Re-evaluate the same plan
- Re-think the same chain-of-thought
Because nothing is cached semantically — only text logs.
AILANG turns every expensive AI output into:
sem_frame: {
opaque = full output,
embedding = semantic summary,
meta = structured tags,
ver = version
}
That means:
- Agents build on top of prior results (incrementally updating frames)
- Expensive steps are automatically cached with semantic identity
- Retrieval is semantic, not string-keyed
- Computation becomes refined, not re-generated
This is exactly how human teams collaborate: they share state, not just messages.
C. Deterministic Multi-Agent Concurrency
Every multi-agent system today has the same failure mode:
- Two agents race
- Both regenerate incompatible plans
- One's update overwrites the other
Because languages have no notion of "semantic shared state".
AILANG's CAS over serialized sem_frame gives atomic, linearizable evolution:
update_frame(key, f)
→ either updates with f(cur)
→ or fails with Conflict(cur)
This makes multi-agent planning, coordination, and simulation correct rather than probabilistic chaos.
This is not something you can bolt onto Python or JS.
D. The Cache Becomes the World Model
In AILANG:
plan:goal-123 -- Planning state
world:region-5 -- Simulation state
doc:user-42 -- Document state
beliefs:agent-7 -- Agent beliefs
are all just sem_frame keys.
This unifies:
- World state
- Plans
- Computations
- Memories
- Intermediate results
- Multi-agent beliefs
into one substrate.
This is the closest thing to a cognitive kernel a language has ever attempted.
E. Agents Store Thoughts as Embeddings
By storing embeddings inside sem_frame, you give agents:
- A continuous semantic map of their shared state
- A way to measure similarity across thoughts
- A mechanism to retrieve related frames
- A cross-agent cognitive coordinate system
This turns your entire system into a context + latent retrieval engine.
Every other language relies on bolted-on vector DBs. AILANG builds it into the language's core.
Why This Creates a Long-Term Moat
Here is the crucial part:
Languages do not retroactively adopt new semantic primitives. Those primitives define the language.
- Python cannot add coordinated multi-agent shared semantic memory
- Go cannot add semantic frames into its type system
- JavaScript cannot introduce effect-tracked semantic state
- Rust cannot add AI-native memory semantics without destroying ownership
- Elixir/Erlang's actor model is message-only, not stateful
But AILANG can, because it's being designed right now around AI-native computation.
A. Unified Foundation for Every Library
"Semantic cache as language primitive" becomes the foundation for every AILANG library.
You get unified APIs for:
| Use Case | Traditional Stack | AILANG |
|---|---|---|
| LLM output cache | Custom Redis keys | SharedMem |
| Vector search | Pinecone/Weaviate | sem_frame.embedding |
| Multi-agent memory | Custom protocols | SharedMem.cas |
| Simulation world state | Game-specific | sem_frame |
| Plan orchestration | Message queues | update_frame |
| Offline/online consistency | Complex sync | CAS semantics |
Other languages will always be fragmented here.
B. Agents Learn from Consistent Memory Traces
Because SharedMem is effect-tracked:
- Training data includes memory access patterns
- "Good update" vs "bad update" is observable
- Agents can learn optimal caching and coordination
In other languages, caches are invisible — models cannot learn how to use them.
This gives AILANG agents new learning channels other languages don't support.
C. Deterministic Snapshots Enable Reproducible AI
AILANG's combination of:
- Deterministic effect model
- CAS semantics
- Fully recordable state transitions
means AILANG agents can be:
- Replayed — exact reproduction of behavior
- Simulated — test scenarios deterministically
- Audited — verify what happened and why
- Checkpointed — save and restore cognitive state
No LLM system built on Python/JS has clean replay semantics — hidden caches and random state leak everywhere.
D. The Standard Library of Cognition
SharedMem + sem_frame + embedder is:
- The memory
- The scratchpad
- The knowledge base
- The shared context
- The plan history
- The inter-agent medium
All languages have file I/O, network I/O, etc. Only AILANG will have semantic state I/O as a primitive.
That's a category-defining feature.
The Killer Analogy
Shared semantic state is to AI agents what shared GPU global memory is to CUDA kernels.
Before CUDA: GPU threads had no shared space → limited coordination. After CUDA: Shared memory → massive unlock in speed + capability.
AILANG is doing the same but for cooperating minds.
The Five Unique Gifts
| Gift | What It Enables |
|---|---|
| AI-Native Concurrency | Deterministic CAS-based cognitive coordination |
| Unified World Model | Plans, beliefs, computations, documents — same type, same substrate |
| Latent-Space Collaboration | Agents continue each other's semantic state, not re-interpret text |
| Trainable Memory Interactions | Cache usage and state evolution become part of observable behavior |
| Zero-Fragmentation Architecture | No mixing Redis + Kafka + Pinecone + HTTP queues; one SharedMem |
Implementation: DX-15
The shared semantic state vision is being implemented as DX-15 in AILANG v0.5.x:
Core Components
| Component | Purpose |
|---|---|
SharedMem effect | Abstract access to shared/persistent state |
sem_frame type | Canonical payload: id, version, timestamp, embedding, metadata, opaque |
AI.embed effect | Generate embeddings via pluggable backends |
SharedCache interface | Backend-agnostic (memory, Redis, Firestore) |
Example: Multi-Agent Plan Coordination
-- Planner creates initial plan
let frame = with_sem_cache("plan:" ++ goal_id, build_plan_for_goal)
-- Critic reads and annotates
match update_frame("plan:" ++ goal_id, add_critique) {
Updated(frame) => log("critique added"),
Conflict(frame) => retry_with_backoff(),
Missing => create_initial_plan()
}
-- Executor reads final plan and executes
match load_frame("plan:" ++ goal_id) {
Some(frame) => execute_plan(frame.opaque),
None => wait_for_plan()
}
All agents share the same sem_frame via SharedMem — no message queues, no serialization drift, no lost updates.
Design Documents
Summary
AILANG is the first language designed for distributed cognition.
Where other languages treat AI as an external service to call, AILANG treats AI agents as first-class citizens that:
- Share semantic memory natively
- Coordinate through CAS-protected state
- Learn from their memory access patterns
- Think together in latent space
No general-purpose language can retrofit this.
When the coder is the model, the language must think like one too.