Agentic Layer

The Perfect Blend

Synerise System Agent blends agents and Behavioral AI into Working AI — turning natural language into actions that drive productivity and growth, with your team in control.

Productivity built into the platform. The platform executes, your team approves.

Orchestrating Vendors → Agents

Your team keeps the strategy. Synerise System Agent takes the coordination.

Yesterday

Multi-Party Coordination Tax

Every campaign required hand-offs across separate vendors and tools — each with its own briefing, ticketing, and turnaround time.

  • Creative agency
  • Data & analytics partner
  • Marketing automation vendor
  • Personalization platform
  • Email service provider
  • Reporting & BI tool
Today

End-to-End Agentic Execution

One agentic layer runs the loop in parallel, keeping humans in control of strategy and approvals.

  • SUGGESTProposes the next-best campaign or experiment from real behavioral signals.
  • PREPAREAssembles audiences, content variants, and assets ready to ship.
  • EXECUTELaunches across channels with the right cadence and guardrails.
  • SUMMARIZECloses the loop with a clear readout of what worked and what's next.

Steps run in parallel wherever possible — and that's where the structural productivity gain comes from.

Behavioral AI Infrastructure

Every decision grounded in behavior

Synerise predicts the next action of every person or object from real-time behavioral signals and activates it in any channel, human or agentic. Sub-millisecond inference, self-supervised foundation model. Open by design: MCP, A2A.

2.6B

Decisions per day

65K

AI decisions per second

229B+

Big Data queries per month

On proprietary systems

Schema-free modeling: entities and relationships evolve without migrations
Sub-millisecond inference · self-supervised foundation model

What It Can Do

Four working modes. Growing autonomy.

Each mode asks more of the agent than the last — from answering with data to creating assets the operator then approves.

Show

Data and visualizations on demand

01

Monitor

Platform health and issues to fix

02

Explain

Configuration logic and root causes

03

Build and teach

New assets and expert knowledge

Goes through approval

04

Growing agent autonomy

Agentic Orchestration

From one sentence to a complete workflow

Prompt

“Build a workflow where we will prepare an email campaign with news for the FAN group and after a week we will check whether they have reached the SUPER FAN level and if so, we will send them a thank you note.”

Agent · a few seconds

Audience

FAN segment

Email

News for the group

Wait

7 days

Profile filter

SUPER FAN level?

Thank-you note

Email

The draft lands on the canvas — the operator adds content, approves assets and launches. A complete journey in seconds.

Workflow on the canvas

A draft on the canvas. The decision is yours.

Audience, email, delay, profile filter and thank-you note: a complete journey in a few seconds.

Governance

Works for you, never beyond your permissions

The agent proposes; the operator decides. Before anything is created, the operator sees exactly what will exist.

Operator

Requests a change

Agent

Prepares the asset

Operator approval

  • DenyEsc
  • Allow once↵
  • Always allow⌘ ↵

Write to Synerise

Aggregates · expressions · segmentations

Within role permissions

The operator sees exactly what will be created before it exists — and the agent acts only within the operator's permissions.

Approval in the product

Permission before every write

Every write action waits for approval. The operator sees exactly what will be created and decides: deny, allow once or always allow.

Strategic Direction

Agentic AI as Strategic Direction

Synerise treats agentic automation not as an experiment, but as a core strategic pillar. It shapes how we build products, allocate resources, and define our long-term vision. This commitment is formalized across the organization — from dedicated policy frameworks to cross-functional teams driving execution.

01

Policy

A formal agentic AI policy governs how agents operate within the platform — defining boundaries, data access rules, compliance requirements, and ethical guardrails that ensure responsible automation at scale.

02

Roadmap

A dedicated agentic automation roadmap drives product development — with clearly defined milestones for agent capabilities, integration protocols, model support, and quality evaluation frameworks.

03

Teams

Cross-functional teams are organized around the agent ecosystem — combining AI research, platform engineering, product design, and domain expertise to deliver cohesive agent experiences.

04

Strategy

Agentic automation is embedded in the company's strategic plan — aligning business objectives, go-to-market positioning, and technology investments around the vision of AI-driven productivity.

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Two Surfaces

An assistant in the platform. MCP in your agent.

Synerise System Agent works inside the platform. The Synerise MCP Server brings the same tools to the agent your engineers already use.

Inside Synerise

Synerise System Agent

  • Every Synerise user has their own assistant
  • Show · Monitor · Explain · Build
  • Every write waits for operator approval

The platform operator doesn't have to leave Synerise: the assistant lives in the product, knows the workspace and works with whatever that role can access.

Synerise UI path

Outside Synerise

Synerise MCP Server

  • Claude Code, Codex, Cursor, ChatGPT
  • Your own agent, your stack
  • Same tools, same permissions

The engineering team doesn't have to open Synerise: it connects Claude Code, Codex, Cursor or its own agent through MCP.

MCP + Skills path

First-party behavioral data

Report, build and experiment on your workspace data from either surface

Reads and writes

Both surfaces work where the data lives. The workspace admin grants access, and workspace roles set what each user can do.

Architecture

Three kinds of operators, one governance layer

People in Synerise remain the main operators. Agents and API integrations work on the same objects alongside them.

People

Synerise UI

Marketing, loyalty and campaign teams run the platform and approve what agents prepare.

Agents

MCP + Skills

Coding agents and in-house agents query the workspace and work on its objects for engineering and analytics teams.

Integrations

API

Architects and integrators create, edit and activate objects through the API, from their own code and pipelines.

Governance layer

Role-based access · Approval workflows · Audit log · Object versioning · Rollback

Synerise platform

Automations · Segmentations · Templates · Recommendations · Brickworks · Loyalty

Every path works within workspace roles, with one audit trail and one version history. Agent writes also wait for operator approval.

Interoperability

Open by design

Synerise connects to external agents through open standards. Your own agent works on the same behavioral layer the platform runs on.

Model Context Protocol (MCP)

The Synerise MCP Server exposes workspace data and platform actions as MCP tools. Any MCP client can call them, including the agent your team builds.

Agent Skills

Synerise skills follow the open Agent Skills standard: one set of skills, packaged for each coding agent. A plugin packages them with the Synerise MCP Server as a single install for each coding agent.

Agent2Agent (A2A)

Synerise agents support Agent2Agent, the open protocol agents from different vendors use to exchange tasks and results.

Your team can switch the model or the agent framework and keep the same Synerise integration.

Prompt Library

120 ready-to-use prompts. Find the right starting point.

Browse workspace prompts by capability, task, role and output. Copy the full prompt, replace its placeholders, and use it in Synerise System Agent.

A reference library, not a live agent interface. The agent is in private preview on selected workspaces.

Open the full catalog, writing tips and playbooks

Filters combine. Use a family for the big picture, an area for a capability, and tags for your task.

Results: 120 / 120

Replace highlighted [square brackets] with values from your workspace.

  1. REC-01

    DiscoveryREC

    List active recommendation campaigns

    List all active recommendation campaigns.

    Intent:
    Explore
    Persona:
    Merchandiser
    Output:
    Action list
  2. REC-02

    DiscoveryREC

    Inspect campaign configuration

    Show the configuration of campaign [campaign name]: recommendation type, feed, filters and number of items.

    Intent:
    Explore
    Persona:
    Merchandiser
    Output:
    Table
  3. REC-03

    DiscoveryREC

    Explore available recommendation types

    Which recommendation types are available for feed [feed name]: personalized, similar items, complementary, frequently bought together, visual similarity?

    Intent:
    Explore
    Persona:
    Merchandiser
    Output:
    Table
  4. REC-04

    DiscoveryREC

    Review monthly campaign performance

    How is campaign [campaign name] performing this month?

    Intent:
    Measure
    Persona:
    Marketer
    Output:
    Table
  5. REC-05

    DiscoveryREC

    Rank campaigns by revenue

    Show clicks, CTR, revenue and conversions for all recommendation campaigns in the last 30 days, sorted by revenue.

    Intent:
    Measure
    Persona:
    Analyst
    Output:
    Table
  6. REC-06

    DiscoveryREC

    Track campaign CTR over time

    Plot daily CTR for campaign [campaign name] over the last 8 weeks.

    Intent:
    Measure
    Persona:
    Analyst
    Output:
    Chart
  7. REC-07

    DiscoveryREC

    Compare app and web campaign revenue

    Compare revenue from [app home screen campaign] and [web homepage campaign], last month vs. the month before.

    Intent:
    Measure
    Persona:
    Marketer
    Output:
    Chart
  8. REC-08

    DiscoveryREC

    Find top-revenue recommended products

    Which products generate the most revenue in campaign [campaign name] this quarter?

    Intent:
    Measure
    Persona:
    Merchandiser
    Output:
    Table
  9. REC-09

    DiscoveryREC

    Find low-CTR recommendation campaigns

    Which recommendation campaigns had CTR below 1% in the last 14 days?

    Intent:
    Measure
    Persona:
    Merchandiser
    Output:
    Action list
  10. REC-10

    DiscoveryREC

    Preview recommendations for a product

    Preview what campaign [campaign name] would recommend for product [product ID].

    Intent:
    Preview
    Persona:
    Merchandiser
    Output:
    Carousel
  11. REC-11

    DiscoveryREC

    Preview a customer's personalized recommendations

    Show personalized recommendations for customer [UUID]. I want to see what they get on the app home screen.

    Intent:
    Preview
    Persona:
    Marketer
    Output:
    Carousel
  12. REC-12

    DiscoveryREC

    Preview article recommendations for a reader

    Preview what campaign [Articles for you] returns for reader [UUID].

    Intent:
    Preview
    Persona:
    Marketer
    Output:
    Carousel

Showing: 12 / 120

Next step

Run these prompts on your own workspace

An in-platform assistant for operators, MCP for the engineering team. Same context, same permissions, same audit trail.

First step: pick three prompts from the library above and run them on your own data.

Scenes with human were generated using AI and do not depict real people.