entigram-ai 1.3.1

Schema-first semantic governance layer for enterprise agents

Entigram: The Semantic Governance Layer for Enterprise Agents

Entigram is a schema-first control plane for enterprise agents that grounds agent behavior in verified domain models, approved semantic alignments, and auditable state transitions.

It provides the infrastructure to build constrained autonomy, ensuring that agents operate across fragmented enterprise systems without inventing fields, joins, entities, or state transitions.

🎯 The Entigram Thesis

Enterprise agent adoption fails when agents lack trustworthy domain context and enforceable schema boundaries. Entigram addresses this by sitting between your agents and your enterprise state.

Defensible Grounding: Entigram prevents unsupported concepts and unverified mappings from entering operational agent workflows.

🛠️ Key Capabilities

• Domain Boundaries (Schema): Force agents to operate against explicit Entigram Schemas rather than vague natural-language context.
• Closed-World Reasoning: Automatically reject or quarantine unknown entities, attributes, and relationships.
• Verified Semantic Alignments: Enable cross-domain data federation using approved mappings instead of fuzzy LLM guesses.
• Deterministic Conflict Handling: Transform contradictory agent states into auditable ledger entries for human or policy-driven resolution.
• Expectation Guard: Convert modeled expectations, implementation rules, and validation checks into a runnable pre-handoff agent gate.
• Agent Hydration: Boot agents with exact project state, schemas, alignments, and settled decisions.
• Auditability: Store every alignment and decision in a local SQLite ledger for full provenance and governance.

🚀 Quickstart

1. Initialize a Governance Workspace

python3 -m entigram.cli_runner.etg_cli init --dir my-governed-agent
cd my-governed-agent

2. Define your Schema Contracts (Schema)

Create a schema.lds to define the entities and relationships your agents are allowed to "know."

ENTITY: Supplier
ATTRIBUTES:
  - .id (UUID)
  - name (String)
  - tax_id (String)

3. Hydrate and Launch

Align your agent's state vector with your local domain models:

python3 -m entigram.cli_runner.etg_cli agent --engine Antigravity

Before handoff, verify modeled expectations and record evidence:

python3 -m entigram.cli_runner.etg_cli broker guard

🏗️ How it Fits

Entigram is not an orchestration framework, MCP replacement, graph database, or IAM product. It is the semantic governance layer that complements those systems by providing:

1. Schema Discipline: Validating agent inputs/outputs against a strict Schema.
2. Alignment Gates: Ensuring cross-system joins (e.g., Salesforce Opportunity to Warehouse SKU) use verified mappings.
3. Decision Ledger: Providing a persistent, auditable record of state transitions.

Agent framework
  -> Entigram semantic governance
  -> MCP/tools/connectors/databases
  -> enterprise systems

Existing Layer	Examples	Entigram's Role
Agent orchestration	LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework	Validate domain state, mappings, payloads, and handoffs before agents act
Tool and data access	MCP, API tools, enterprise connectors	Govern tool schemas and block unsupported concepts or unverified mappings
Knowledge and context	RAG, GraphRAG, Neo4j, Stardog, data.world, LlamaIndex	Operationalize only verified concepts, relationships, and alignments
Runtime governance	RunAgents, Okta, policy engines, approval systems	Supply semantic policy signals and provenance for tool/action decisions
Observability	Tracing, OpenTelemetry, agent logs	Add semantic provenance: schema, alignment, evidence, conflict, and decision IDs

🔒 Operational Principle

Discovery creates proposals, not operational facts.

Agents and routers may suggest alignments from schema similarity, partner data, or field names, but those proposals do not drive cross-domain joins until they are explicitly authorized with trusted evidence.

📈 Best-Fit Use Cases

• Partner Reconciliation: Normalizing and aligning external supplier data with internal systems.
• Cross-Domain Integration: Linking CRM data (Salesforce) to supply-chain or inventory forecasting.
• Regulated Data Extraction: Clinical/EHR extraction with strict validation and conflict gates.
• Governance for Multi-Agent Ops: Auditing the "handoff" state between different specialized agents.

⚖️ License

Entigram Core is Open Source under the Apache License 2.0.

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Entigram: Grounding agentic autonomy in enterprise reality.