The digital landscape has been defined by the client-server paradigm: a world of centralized silos where your data is rented, intelligence is actually a remote API call, and meaning is lost in translation between proprietary schemas.
As we move toward a future of autonomous agents and pervasive AI, this centralized model has become the primary bottleneck. True scalability requires more than just faster servers and bigger datacenters. It requires a fundamental shift in how we address, store, and reason over information. The round trip to the center needed for each action is wasteful at least and downright harmful at worst.
However, at the root of any large decentralized system is the problem of meaning. In an ideal world, every microservice and client app agrees on what a "Customer" or "Invoice" is. This shared dictionary ensures seamless data interoperability. In reality, different systems have ontological differences in their representations or formalisms. To the Billing app, a "User" is a credit card and an address. To the Analytics app, a "User" is a session ID and a clickstream.
DDD solves ontological differences through bounded contexts. You don't force a single, monolithic "User" model on the whole company. Instead, you allow each context to have its own strict dictionary.
So, how can you have a unified persistence layer if ontologies differ?
You store data at its lowest, immutable level - often as event sourcing, a ledger of things that happened, or formally, a sequential log of state change events rather than just the current state. The unified layer stores facts (e.g., User_Created_Order, Vendor_Refunded_Payment), while each of the different applications/bounded contexts project those facts into their own ontological shapes.
Intensions, Extensions and Horns
To build a "knowledge base" (not just a database), the system needs to understand the difference between raw facts and derived truths. Both live together:
Extensional Data: This is your explicit data. The actual rows in your database. (e.g., Alice is an Admin, Bob reports to Alice).
Intensional Data: This is implicit data defined by rules. It isn’t always stored; it is derived on the fly. (e.g., A user has access to a document IFF the user is an Admin OR the user created the document).
Horn Clauses: This is how you formalize intensional data. A Horn clause is a rule from logic programming (like Prolog or Datalog) structured as Head :- Body (Head or consequent is true if Body or antecedent is true).
By storing extensional facts in your unified database and distributing intensional Horn clauses to your applications, your apps can logically deduce complex relationships (like permissions or ontological translations) without needing massive complex SQL joins.
Say, we have figured out how to model and store both facts and rules as aforementioned horn clauses in the database layer itself instead of the code. What else would we need to make it a 'unified' persistence layer? I would say that this logic needs to run everywhere without duplicating code across backends and frontends. TypeScript can be isomorphic in this sense and can help here.
TypeScript can act as the compile-time enforcer of your shared ontology. By defining your schemas and your Horn clause rules in TS, you ensure that the local client database and the remote cloud database or p2p database speak the exact same language. The isomorphic database engine and its data live both in the cloud and on the local device (browser/mobile). Changes made locally are synced to the unified persistence layer via a sync engine and/or CRDTs (Conflict-Free Replicated Data Types).
Such a distributed database and pure-TypeScript SQL query engine actually exist. You can also use ElectricSQL active-active replication or couchdb-pouchdb replication or a prisma-queries-over-rpc implementation to a similar effect if you do not need a fully p2p database for your use case.
LLMs, Agents and Orchestrators
If you have an isomorphic database, every device has a local, extensional replica of the data it cares about. If you have a TypeScript/TypeQL rule engine, every device has the intensional logic to understand that data.
Local-First, On-Device LLMs (e.g., Gemma): Since the device has a local database replica, the on-device LLM operates with zero latency and full privacy. It acts as a local agent. It uses Retrieval-Augmented Generation (RAG) against the local isomorphic SQLite/PouchDB/Optimystic database. Because it understands the shared ontology, it can query the local DB, infer meaning using the Horn clauses, and summarize data or execute actions without ever hitting a network.
Remote LLMs (e.g., Gemini): The heavy-weight cloud LLM sits on top of the Unified Persistence Layer. While Gemma handles local, context-specific tasks for a single user, Gemini handles macro-level reasoning across the entire enterprise graph, identifying cross-boundary ontological connections that a single client device doesn't have the data to see.
Intelligence Router: When the user asks a question or triggers an action, an orchestrator evaluates the compute cost and privacy needs. If the data exists in the local replica and requires lightweight reasoning, it routes to a local Gemma/GLM/Qwen/GPT-oss/MiniMax etc. If the query requires enterprise-wide aggregation, complex intensional derivations across bounded contexts, or generation capabilities beyond the edge device, it routes the structured request to a remote Gemini/Claude/ChatGPT/Kimi etc. A more complex cross-device agentic orchestrator can be built using NetworkX (Python) or Graphology (TypeScript) along with LangGraph (py/ts).
That's it from a 1000 ft bird's eye view:
If you have all this architecturally in place, you have a system that is instantly responsive (local-first), mathematically sound without hallucinations (Horn clauses/TS), logically segregated but physically unified (DDD + unified persistence), and capable of neuro-symbolic reasoning (LLMs + relational data) at both the edge and the cloud. If you are wondering about its implementation details, let's unfurl it further:
Being Intelligent vs Calling Strangers
In shallow use cases, the above can be achieved using TypeDB (definite horn clauses and logical reasoning), Postgres-ElectricSQL (synced tables)/ CouchDB-PouchDB (synced documents) and LangGraph (orchestration of multi-actor apps with LLMs). However that's not actually creating distributed intelligence backed by a universally scalable knowledge base. Far from it, it is still just our devices pretending be intelligent by phoning home intelligent stranger (who are also officially used for mass-surveillance, remote weapons and war orchestration) all our personal data, thoughts and ideas.
Speaking of evil strangers watching over us, empowering genocidal wars, and corrupting the minds of the masses, let's think about how Palantíri folks architected such a unified intelligence. Palantir’s architecture, specifically within their Foundry and AIP (Artificial Intelligence Platform) products, revolves around the concept that a database should not just be a "System of Record" (storing rows), but a "System of Action" (mapping decisions).
An ontology layer in knowledge engineering acts as a formal, machine-understandable model that structures domain concepts, properties, and relationships to facilitate data integration, reasoning, and AI application development. It bridges the gap between raw data sources (SQL tables, APIs) and semantic understanding, serving as a "brain" that enables automated reasoning, data lineage, and context-aware queries.
Their ontology layer is essentially a high-scale, operationalized digital twin. It sits on top of disparate data sources (ERP, CRM, IoT, etc.) and transforms raw tables into a semantic graph of "Nouns" and "Verbs." Here's the plumbing in brief:
Magritte (Ingestion): Connectors pull data from silos (S3, SQL, HDFS).
Foundry Pipelines: Data is cleaned and transformed into "backing datasets."
Ontology Mapping: These datasets are "mapped" to Object Types.
The API Gateway: Palantir exposes the Ontology via the OSDK (Ontology SDK).
The Semantic Layer (The "Nouns"):
Instead of querying a table named tbl_inv_01, an analyst or an AI agent interacts with an object called "Invoice."
Object Types: Real-world entities (e.g., Aircraft, Employee, Part).
Properties: Attributes of those objects (e.g., Tail Number, Satus).
Link Types: The relationships between them (e.g., Part -> is installed on -> Aircraft).
The Kinetic Layer (The "Verbs")
This is where Palantir differs from a standard Knowledge Graph. It doesn't just store relationships; it encodes Actions.
Action Types: These are governed "Verbs" (e.g., "Reorder Part," "Approve Invoice").
Write-Back: When an action is triggered, Palantir’s Action Engine handles the logic of updating the internal ontology and writing back to the source system (e.g., SAP or Oracle) via webhooks or the Phonograph service.
This allows LLMs and developers to "reason" over the ontology with OSDK code like client.objects.Invoice.get(id) instead of writing complex SQL joins. Because the data is already structured into Objects and Actions, an LLM doesn't have to guess the SQL schema. It simply says, "Find all Invoices over $10k and trigger the Flag for Review Action."
Palantir’s success isn't in their "AI" (the LLMs), but in their shared ontology. By forcing every disparate system into a shared "Language of the Business," they’ve created a environment where distributed intelligence (AIP Agents) can finally be useful because they are speaking a language the enterprise actually understands . This language of business is essentially the same as the "ubiquitous language" concept from DDD mentioned above but mapped across independently built software systems instead of just the one being currently built.
This is at least a bit better at "being intelligent" than just calling LLMs with a prompt and relevant data from usual RAG pipelines. Their platform is 'knowledgeable' about the enterprise domains they integrate, the 'brain' orchestrates arbitrarily large number of integrated systems, and their LLMs are effectively more 'intelligent' because of the platform's ontology layer and the accompanying high-level unified data access SDKs.
A further step towards being intelligent, apart from local models and LLMs, logical reasoning over facts and rules and a shared ontology layer, is tackling 'the problem of meaning' mentioned in the beginning of this article. Entity-relation style schemas are limited in their semantic expressiveness. The foundation of a unified knowledge base is not a database schema, but a formal semiotic system. Modern architectures must move beyond the "table and row" mentality towards a hypergraph-based mental picture.
I have been using the helpful mnemonic MACER (Metatype, Archetype, Concept, Entity, Relation**)** for organizing large number of concepts with types, inheritance hierarchies, and Entity-Relation schemas and for effectively building a formal semiotic system into the architecture. Metatypes and Archetypes act as the Signifiers (the structural rules), while Concepts and Entities are the Signified (the actual data instances).
This layered ontology is what allows a highly decentralized system to maintain coherence. Even if two edge devices have radically different local datasets (Entities), they share the same metatypes or types of types (e.g. temporal, monetary, cadastral, medical). If they have similar 'behavior', they have the same archetypes (e.g. ephemeral, numerical, monoidal, categorical, algebraic). This guarantees that when Agent A asks Agent B for a concept C, they are speaking the exact same structural language to describe the same thing and/or the same 'type of thing' instead of the specific table row or document representing the thing.
TypeDB is uniquely suited for this because its native schema is an ontology. The MACER layer and intensional rules (Horn clauses) can live here as the absolute source of truth. Unlike traditional SQL, TypeQL allows for the expression of Horn Clauses and logical inference:
e.g. Author(x, y) ^ Contributor(y, z) => Related(x, z)
This ensures that the "Logic" of the database is not trapped in application code but lives within the data layer itself. However, graph databases and logical inference engines can be computationally expensive for simple UI reads. By "projecting" a slice of the fully resolved, logically inferred states from TypeDB into PostgreSQL / SQLite / Optimystic, you create high-speed, extensional materialized views or unlogged tables. Postgres / SQLite / Optimystic becomes the highly optimized read-model, while TypeDB remains the brain. The DB sync layer provides the byte-level sync and provide the immediate, latency-free UI updates (optimistic concurrency) required for a smooth user experience, gracefully handling the eventual consistency of the P2P data layer. Using TypeQL for global SSOT & SQL for device-specific projections is an elegant way to solve this performance vs. reasoning tradeoff. With all this in place, a developer or an LLM agent is not just syncing data but also syncing intent and knowledge across a decentralized mesh.
Distributed Intelligence and Liquid Knowledge
For a network level and cross-device orchestration of agents, here is how an orchestrator like LangGraph and a graph library like Graphology in TS (or NetworkX in Py) converge to orchestrate local-first models (e.g. Gemma) and remote models (e.g. Gemini) across the network.
The Micro View: LangGraph (Intra-Agent State)
On each node (whether it's a mobile device or a cloud server), an LLM operates within a LangGraph execution environment.
The Local Edge (Gemma + PouchDB/SQLite): Gemma runs locally, grounded in the user's specific, subset data replica. LangGraph manages its internal state machine (e.g., Observe -> Reason -> Query Local DB -> Act).
The Cloud Node (Gemini + TypeDB/Postgres): Gemini runs in the cloud, grounded in the full enterprise graph. Its LangGraph cycle is similar but has access to massive, cross-boundary compute and the complete MACER ontology.
The Macro View: Graphology (Inter-Agent Topology)
If LangGraph is how an agent thinks, Graphology is how the swarm communicates. You treat the entire distributed system as a mathematical graph where nodes are Agents and edges are communication channels (permissions, latency, semantic proximity).
Agent Discovery & Routing: You can use Graphology (and something like FRET) to maintain the topology of the intelligence mesh. If a local Gemma agent hits an "epistemological wall" (it encounters a query requiring data or reasoning beyond its local projection), it doesn't just blindly call an API. It uses Graphology algorithms to find the optimal path to a peer or cloud agent that does have the required context.
Semantic Routing: Because of our shared MACER ontology, the Graphology graph can weight its edges based on semantic domains. If an agent needs to resolve a complex security incident, Graphology routes the sub-task to the specific agent node (likely a Gemini-backed node) that specializes in the "Security" Archetype.
Distributed RAG (HypergraphRAG): When an agent initiates a RAG pipeline, it doesn't just query a single vector store. The orchestrator broadcasts the query intention across the Graphology topology. Local devices process what they can privately; cloud nodes process the heavy aggregations. The results are synthesized back to the requesting node.
Summarizing the flow of execution:
User Request: User asks complex question.
Local Attempt: The local Gemma LangGraph agent attempts to solve it or some part of it using the local PouchDB/ElectricSQL/Optimystic data.
Orchestration (Graphology): If it fails or needs more power, it creates a sub-task and queries the Graphology registry for the nearest capable node (e.g., the Cloud Gemini node).
Remote Execution: The Gemini node receives the strictly-typed MACER intent, queries the TypeDB SSOT, infers the answer, and passes the state back. It may potentially update the shared TypeDB ontology (the Metatypes / Archetypes). These updates then trickle back down via Optimystic / ElectricSQL / PouchDB to all edge nodes.
Resolution: The local agent finalizes the task and Optimystic updates the UI.
At this point, how would you envision structuring the payload when Agent A (e.g. Gemma) hands off a complex sub-task to Agent B (e.g. Gemini)?
A CID, or an identifier based on the concept's cryptographic hash can play a significant role here. By using CIDs, you decouple the identity of a concept or a piece of logic from its location. If Agent A (Gemma) sends a task to Agent B (Gemini) referring to CID(Invoice_Archetype_V1), Agent B doesn't need to ask "what is that?"—it either has it in its local cache or fetches it from the distributed unified persistence layer. Agents should use the CIDs to write well-typed and validated JSON state, TypeScript for imperative steps and/or TypeQL code for logical reasoning, and pass it to each other for execution in sandboxed and pre-validated environments.
When a LangGraph agent transitions state across the network (from local-on-device to cloud-central), the payload looks like a "Semantic Packet":
- The Context (CID-Linked JSON State)
The state is a merkle-dag of the current MACER instances, not just a blob.
Mechanism: Every entity or concept in our system has a unique CID.
Benefit: Instead of sending the full data of a 50MB security log, Agent A sends the CID of the log. Agent B, if it already has that data in its Postgres projection, simply resolves it. This minimizes network overhead in P2P/Distributed environments.
- The Instruction (TypeScript + TypeQL)
Agents are sending executable intent, not just a prompt.
TypeQL: Used to describe the declarative search or inference. (e.g., "Find all Entities related to this CID via the AuthoredBy relation").
TypeScript: Used for the imperative transformation. (e.g., "Once the entities are found, filter them using this specific logic and return the result in this schema").
Validation: Because the JSON state is well-typed, the receiving agent can run a TypeSafe check before even waking up the LLM, preventing "hallucinated actions."
- The Nuance (Natural Language) The "Rest" is the fuzzy logic where LLMs excel.
Purpose: Describing the goal, the persona, or the qualitative assessment.
Interaction: The LLM reads the Natural Language to understand the Why, and then executes the TypeQL/TypeScript to handle the What and How.
Ontology Bootstrap: The Cold Start of Meaning
In this system, a major hurdle is ensuring that when a new device joins the network, it can quickly resolve the CIDs for the core MACER ontology without downloading the entire global state.
This can be treated like a multi-level CPU cache with a dual-track strategy for essential and contextual bootstrap.
The AOT "Essential" Layer: Device-as-Archetype
By defining "essential concepts" based on device type, we are applying the Archetype layer of the MACER model to the hardware itself.
The Profile Map: A mobile device (e.g., a field agent's phone) might pre-fetch the Location, Identity, and SensorData Archetypes. A server-side node might pre-fetch the Aggregation, Audit, and SecuritySOAR Metatypes.
The Semantic Skeleton: This AOT fetch provides the "grammar" of the system. Even if the device has no data (entities/relations), it understands the structure of the world it is about to encounter.
Implementation: These can be delivered as a Genesis Snapshot via ElectricSQL/Optimystic/PouchDB during the initial handshake, ensuring the local SQLite instance is "schema-ready" before the first user interaction.
The JIT "Contextual" Layer: Event-Driven Hydration
The "Contextual" layer acts as the Dynamic Working Set.
Event-Triggered Resolution: As the user moves through the application, your event-tracking (potentially via a local Redux-style store or Optimystic state) identifies CIDs that aren't in the local L1 cache.
Lazy Discovery: The system issues a "Semantic Seek" across the Graphology mesh.
- Example: If a user opens an "Invoice" view, the device detects the
Invoice_CID. It lazily fetches the Currency, TaxRule, and Vendor concepts associated with that specific instance.
Pruning: To prevent local "ontology bloat," you can implement a Least-Recently-Used (LRU) Pruning mechanism on the local CID-store, keeping the on-device Gemma model focused only on the current task's semantic neighborhood.
The Unified Access & Permission Architecture
In a distributed intelligence model, authorization becomes a logic problem, as opposed to a configuration problem.
Logic-Based Permissions: Your TypeDB (SSOT) contains Horn clauses that define access. Instead of checking a "Role" table, the system asks the graph: is_authorized(UserCID, ActionCID, ResourceCID)?.
Isomorphic Verification: * Local: Gemma checks the local TypeQL rules to see if it should show a piece of data.
- Remote: When the task is handed off to Gemini, the cloud node re-validates the logical path against the master TypeDB instance.
Auditability: Since every state transition is linked to a CID and every step is a logical TypeQL/TS instruction, you have a perfect, immutable audit trail of why an AI agent made a specific decision.
The Result: A Liquid Knowledge Base
When you put this together with the agentic DI layer, you get a system that feels fluid in the sense that relevant knowledge flows across it like liquid does:
The Local: Operates on the AOT Essential Concepts for near zero-latency UI interactions.
The Observer: Tracks events and "warms up" the JIT contextual concepts in the background via the P2P sync engine.
The Hand-off: When a task is escalated to Remote, the "well-typed JSON state" includes the CIDs of the context Gemma was just looking at. Because the Cloud node already has the "Global" ontology, it doesn't need to ask for definitions—it simply looks up the CIDs in TypeDB and returns the logical result.
Unified Knowledge Access + Distributed Intelligence Summary:
In this system, one can think of the network itself as the persistence layer, and the devices as the specialized neurons.
By using TypeQL as the logic layer and CIDs as the addressing mechanism, the system moves past API calls and into shared latent spaces. The TypeScript / TypeQL instructions ensure that even if the agents are heterogeneous (a small Gemma 4 vs. a massive Gemini), the logic remains isomorphic and deterministic across the entire distributed intelligence mesh.
Layer | Component | Function |
Meaning | MACER / Semiotics | Standardizes how signs and data relate via CIDs. |
Logic | TypeQL / Postgres | Declares the TypeQL rules (SSOT) and maps them to SQL constraints for speed when applicable |
Execution | ElectricSQL / Quereus | Synchronizes the physical bytes and optimistic state. |
Orchestration | Graphology / LangGraph | Routes "Intent Packets" between local and remote agents. |
Hydration | AOT + JIT Fetching | Manages the "Semantic Cache" based on device and event. |
Lastly, this architecture is uniquely resilient to the "Orphaned Node" problem in P2P because a device carries its Essential Ontology AOT, it can still perform sophisticated local reasoning even when completely disconnected from the mesh. It is a high-level response to the "Trilemma of Distributed Systems": balancing Consistency, Availability, and Partition Tolerance but with a modern semantic and agentic twist. By moving from raw data packets to CID-linked semantic packets we bypass several classic P2P bottlenecks while introducing a new set of challenges unique to Distributed Intelligence (DI). However, this is definitely not straightforward to implement and the distributed nature of this system necessitates addressing a few distributed systems problems, which we will discuss in the next part.
