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Rippletide at VivaTech 2026

What VivaTech confirmed about AI agents in production.

At VivaTech 2026, the pattern was clear: AI agent demos are everywhere. The production blocker is deciding which actions should be allowed to execute.

The blocker is no longer model capability. It is decision control.

Rippletide is the Decision Runtime for enterprise AI agents. Your agent proposes an action. Rippletide decides whether it is authorised before anything reaches your systems.

Agents propose. Rippletide authorizes. Only valid decisions execute.

Take the readiness test

What Rippletide makes possible

With Rippletide, enterprises move agents into production with:

  1. 01

    Safer autonomous actions

    Agents act across real systems without relying only on prompts, policies buried in documents, or post-mortem monitoring.

  2. 02

    Deterministic execution control

    Every proposed action is checked against your company's rules, processes, limits, and decision context before it executes.

  3. 03

    Faster agent deployment

    Rippletide builds the operational decision reference from documents, workflows, APIs, logs, and evaluated traces, reducing the time needed to make agents production-ready.

  4. 04

    Explainable decisions

    Every approved, blocked, escalated, or rerouted action comes with a structured decision trace: which facts, which rules, which context, which outcome.

  5. 05

    Enterprise-scale reliability

    The same decision logic is reused across agents, teams, systems, and workflows instead of being rebuilt inside prompts or orchestration code.

Why this matters now

VivaTech 2026 confirmed the market shift from chatbots to agents that execute real business actions. The most important question is no longer whether an agent can complete a task in a demo.

But enterprise adoption stalls at the production threshold.

Not because agents cannot reason. Because companies cannot yet prove that every action is valid, authorised, auditable, and aligned with their operational rules.

Rippletide closes that gap.

Useful next reads

If you are moving agents from demo to production, start with the readiness questions, then go deeper on governance and auditability.

How it works

  1. 01

    Your agent proposes an action

    Refund this customer. Approve this request. Modify this account. Trigger this workflow.

  2. 02

    Rippletide intercepts before execution

    The action is checked against your operational ontology, business rules, process constraints, authority limits, and live context.

  3. 03

    The Decision Runtime resolves the outcome

    Approve, block, escalate, or reroute.

  4. 04

    A decision trace is produced

    Every decision is explainable, replayable, and auditable.

See the layer enterprises need before agents can act

Most AI infrastructure helps agents think, retrieve, plan, or observe. Rippletide handles the missing step: should this action actually execute?

That is the difference between an agent demo and an agent trusted in production.

Trusted for agents by

Where Rippletide exhibited at VivaTech

Rippletide exhibited inside the French Tech Grand Paris Pavilion, Hall 7.2 at Paris Expo Porte de Versailles, on booths 2C11-001 to 2D13-003.

VivaTech 2026 booth locationHALL 7.2Paris Expo Porte de VersaillesOther exhibitorsOther exhibitorsFRENCH TECH GRAND PARIS PAVILION2C2DRippletideBooths 2C11-001to 2D13-003Main entrance
Rippletide booth French Tech Grand Paris Pavilion Other exhibitors
  • Dates: June 17–20, 2026
  • Venue: Paris Expo Porte de Versailles, Paris, France
  • Hall: 7.2, French Tech Grand Paris Pavilion
  • Booths: 2C11-001 to 2D13-003 (aisles 2C and 2D)

Schematic orientation diagram, not to scale. Kept as an archive of the VivaTech 2026 booth location.

Book a post-VivaTech follow-up

Talk with Rippletide about how enterprises can move AI agents from demo-ready workflows to approved production actions with decision control before execution.

Best for: enterprise AI leaders, agent builders, platform teams, automation teams, compliance-sensitive workflows, and teams moving from AI demos to production agents.