Semantica connects the data an AI agent uses, the rules it applies, and the decisions it makes into a queryable, traceable record of the business workflow. In procurement approval, for example, teams can inspect which supplier information informed a recommendation, its recorded rationale, and the subsequent actions linked to it. Once these details are integrated and captured, business teams can review outcomes more easily, developers can investigate errors, and downstream agents can reuse existing context—reducing repeated searches and information lost during handoffs.

This addresses a recurring problem when agents enter real business operations: the task gets completed, but its history remains scattered across conversations, tool-call logs, and separate systems. When a decision needs review or a workflow needs adjustment, teams must reconstruct what happened.

Semantica provides a way to organize this information. It uses knowledge graphs and Context Graphs to connect entities, sources, rules, and decisions, with interfaces for retrieval, relationship tracing, and analysis. It records the evidence and execution history available to the system; it does not expose the model’s internal reasoning. Project overview.

For teams already using agents, integration can begin with a single workflow: identify the decisions that need review, record their inputs and outcomes, and connect those records. The procurement example below shows how this infrastructure can fit into everyday operations.

1. Decide What the Workflow Needs to Capture

Consider a procurement workflow with three stages: shortlist suppliers, approve a recommendation, and create a purchase order. Different people or agents may handle each stage, but they need to share the same business context.

Before integrating Semantica, identify the information to capture:

  • Inputs: Supplier quotations, delivery requirements, and contract versions.

  • Decision basis: The rules applied and the rationale recorded.

  • Outcomes: The recommended supplier, approval status, and resulting order.

  • Connections: Which recommendation entered review and which approval triggered a subsequent action.

Once these details are defined, the graph can help answer concrete questions, such as why an order used Supplier A or which related records need review after a quotation changes.

2. Install Semantica and Record the First Decision

Start with these commands to install the core package and check the environment. Add optional dependencies when you need a particular storage backend or integration.

pip install semantica
semantica doctor

The following procurement example adapts the documented interfaces, recording a supplier selection as a queryable object:

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

selection_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose a supplier for the next procurement cycle",
    reasoning=(
        "Vendor A meets the documented delivery requirements "
        "and offers the lowest evaluated total cost"
    ),
    outcome="selected_vendor_a",
    confidence=0.93,
)

The application supplies reasoning and confidence. These fields make the decision inspectable, but they do not mean Semantica has independently verified the rationale or calibrated the confidence score. Documented decision interfaces.

3. Connect Individual Records into a Workflow

Saving a recommendation alone does not capture the full workflow. The next step is to record the subsequent approval and connect it to the earlier selection.

approval_id = graph.record_decision(
    category="purchase_approval",
    scenario="Review the recommendation for Vendor A",
    reasoning=(
        "The reviewer checked the supplier recommendation "
        "against the procurement requirements"
    ),
    outcome="approved",
    confidence=0.95,
)

graph.add_causal_relationship(
    selection_id,
    approval_id,
    relationship_type="INFLUENCED",
)

chain = graph.trace_decision_chain(approval_id)
impact = graph.analyze_decision_impact(selection_id)

print(chain)
print(impact)

With those connections in place, teams can work backward from an approval to the related supplier selection, or inspect recorded downstream links from a selection. These queries follow relationships explicitly established by the application; they do not independently prove real-world causation. Relationship and tracing examples.

In a real integration, recording can be attached to key workflow events—for example, after a tool call completes, an approval is finalized, or an order is created. Records can then accumulate as the workflow progresses.

4. Let Downstream Agents Reuse Existing Context

The records can support future tasks as well as retrospective review. A procurement agent can retrieve similar historical decisions to understand which factors the team previously considered, reducing the need to start every search from scratch.

precedents = graph.find_similar_decisions(
    "supplier selection with strict delivery requirements",
    max_results=5,
)

print(precedents)

Historical precedents provide context; new decisions still need current quotations, delivery conditions, and business rules. Connecting recording, retrieval, and review creates context that remains useful across tasks.

5. Bring It into Everyday Work Through MCP

To query these records through clients such as Claude Desktop, connect Semantica’s MCP server. The documented configuration is:

{
  "mcpServers": {
    "semantica": {
      "command": "python",
      "args": ["-m", "semantica.mcp_server"]
    }
  }
}

The selected Python environment must have Semantica installed and be connected to the relevant data. Once configured, the client can use exposed tools to query decisions, find precedents, and inspect graph relationships. MCP setup.

For business users, this can make access more natural: retrieve the recorded basis for a procurement recommendation, inspect the upstream relationships of an approval, or find how similar tasks were handled previously.

Starting with one workflow, capturing its key events, and then adding persistent storage, access controls, and further integrations gives teams a practical way to assess whether Semantica reduces review effort, troubleshooting time, and information lost during handoffs.

Sources & references