Typed runtime models for AI operations

Build AI operations that can be inspected and improved.

Semantiv turns agentic work into a typed operating model, so teams can design, test, trace, and improve how AI-supported work actually runs.

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Diagram showing domain model, typed runtime graph, trace data, scenario tests, and operation variants — Product direction

01 Problem

AI work is moving faster than the operating model around it.

Teams are using prompts, agents, scripts, and automation tools, but the meaning, contracts, evidence, and failure boundaries are often implicit.

Observed gap Implicit

What makes this output valid?
Which evidence is missing?
Where did the AI handoff fail?
What can be replayed without rerunning everything?

02 Shift

From automating steps to improving operations.

Workflow tools are useful for moving data through steps. Semantiv focuses on the operating model those steps belong to: contracts, traces, evidence, replay, and candidate variants.

Comparison between automating workflow steps and improving typed AI operations — Differentiation

03 Foundation

A typed runtime graph for agentic work.

The current foundation models the operation while it runs: typed nodes, contracts, runtime events, trace replay, payloads, and localized boundary failures.

Productized Semantiv workbench showing typed runtime graph, trace replay, contracts, evidence, and event stream — Workbench concept

runtime Typed Runtime Graph

validity Contracts

trace Trace Replay

tests Scenario Tests

variants Operation Variants

04 Cases

One pattern across support, finance, sales, legal, product, and operations.

Start where AI work crosses tools, people, documents, data, and decisions, and the team needs to improve the operation without losing traceability.

Case 01 Coding Agents / Software Delivery A devtool startup has an AI coding agent that can read repositories, edit files, open pull requests, request review, and eventually merge approved changes. Risk Enterprise customers want a concrete answer to what the agent is allowed to change before production-impacting actions execute. Case 02 Security Operations / Agentic SOC A security platform is adding agents that triage alerts, investigate incidents, enrich signals, disable users, block IPs, isolate endpoints, and escalate threats. Risk The promise is faster response. The risk is uncontrolled remediation across users, infrastructure, and privileged systems. Case 03 Finance / Accounting Operations A finance automation company has agents that reconcile transactions, classify expenses, prepare reports, draft journal entries, route approvals, and update finance systems. Risk The team wants more automation, but every posting, approval, adjustment, and report change must survive audit. Case 04 Legal / Contract Operations A legal AI product helps in-house teams draft contracts, redline clauses, compare terms, route exceptions, and prepare approval packets. Risk Drafting can be flexible. Approval, clause substitution, external communication, and exception routing need explicit gates.

05 Start

Talk to Semantiv about your AI operation.

Book a consultation to discuss whether the typed runtime approach fits your team, product, or customer problem.

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