Synerise System Agent
A conversational AI layer that transforms natural language into platform-wide actions. Synerise System Agent is the perfect blend of Behavioral AI and agentic automation — a Working AI that orchestrates specialized agents, drives revenue, enforces guardrails, and connects to any LLM provider through an open, interoperable architecture.
Productivity built into the platform. The platform executes, your team approves.
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
Architecture
Three operating paths, one governance layer
Operator persona
UX
Marketing operators, loyalty program managers, campaign teams
Agent persona
MCP + Skills
Claude Code, Cursor, ChatGPT — engineering and analytics teams
DevOps persona
DX
Platform engineers, CI/CD pipelines, enterprise IT
Governance layer
Approval workflows · Audit log · Object versioning · Rollback
Synerise platform
Automations · Segmentations · Templates · Recommendations · Brickworks · Loyalty
The same approvals, audit trail and versioning on every path.
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.
The Perfect Blend
Agents
Agents built for productivity, interoperability and growth
Behavioral AI
AI Search, Ranking, Promotions, Real-time recommendations, Time optimization, Predictions
Working AI
Real, measurable value: understanding your customers, predicting their needs, and communicating with them in the right way at the right time.
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.
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.
Roadmap
A dedicated agentic automation roadmap drives product development — with clearly defined milestones for agent capabilities, integration protocols, model support, and quality evaluation frameworks.
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.
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.
Two Surfaces
An assistant in the platform. MCP in your agent.
Every Synerise user gets their own assistant in the platform. Every engineering team gets the same context through MCP, inside its own agent.
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.
UX path
Outside Synerise
MCP
- Claude Code, Cursor, ChatGPT
- Your own agent, your stack
- Same context, same permissions
The engineering team doesn't have to open Synerise: the same context reaches Claude Code, Cursor or their own agent through MCP.
MCP + Skills and DX paths
Synerise platform
data · objects · actions · permissions — one context for both surfaces
Same platform, same permissions, two ways in.
Specialized Agents
A Family of Expert Agents
Each specialized agent masters a specific domain of the Synerise platform, from workflow automation to revenue optimization.
Automation Agent
Designs and deploys automation workflows — triggers, conditions, and actions — through conversational instructions.
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.
Growing agent autonomy
Show
Data and visualizations on demand
Monitor
Platform health and issues to fix
Explain
Configuration logic and root causes
Build and teach
New assets and expert knowledge
Goes through approval
Prompt Library
34 prompts. Run on a live workspace.
26 demo prompts, followed by 8 deep analyses that combine the modes in a single run. Copy any of them in one click.
33 of them were run on a live workspace via Synerise MCP. Nothing on this list is hypothetical.
01 — Show
Demo prompts · 01 / 04 · 01–07
Data and visualizations on demand
The agent pulls the results and turns them straight into a chart or product cards.
02 — Monitor
Demo prompts · 02 / 04 · 08–13
Platform health and issues to fix
The agent checks what works, what broke and where we're losing results.
03 — Explain
Demo prompts · 03 / 04 · 14–20
Configuration logic and root causes
The agent reads settings and data, then explains them in business terms.
04 — Build and teach
Demo prompts · 04 / 04 · 21–26
New assets and expert knowledge
The agent prepares rules, automations and code. And it teaches you how to use them.
Build
Teach
Deep analyses · 01–08
One question, a full investigation
Each of these runs several tools in one go: data, segmentation, modeling and activation. Analysis 07 ends in a finished dashboard; 08 returns an interactive view.
Interoperability
Open by Design
Synerise embraces open protocols to ensure its agent capabilities integrate seamlessly into any AI ecosystem. MCP and A2A compatibility make Synerise a collaborative platform, not a walled garden.
MCP Server
Synerise exposes its capabilities through the Model Context Protocol, enabling external AI systems to leverage Synerise as a tool provider.
Agent-to-Agent (A2A)
Compatible with the Agent2Agent protocol for seamless interoperability — Synerise agents can collaborate with agents from other platforms.
Claude Code plugin
A native Claude Code plugin makes Synerise a first-class tool inside the engineering agent workflow — query, configure, and act on the behavioral AI infrastructure directly from the terminal.
Any agent reads from and writes to Synerise, developers deploy from Claude Code, Cursor or Codex, and there is no vendor lock-in as your stack evolves.
Growth-Oriented Automation
Synerise System Agents by Growth Areas
Each agent maps to a concrete growth outcome. Whether you need to accelerate team productivity, drive revenue, or integrate seamlessly with external systems — there is a purpose-built agent for it.
01 — Revenue
Synerise Customer Agent
- Agentic search across every channel
- Behavioral signals → conversion
- Ready for zero-click commerce
02 — Productivity
Synerise System Agent
- Agents that can solve complex use cases
- Equipped with full context and operational knowledge
- Marketers as strategists, not integrators
03 — Interoperability
MCP + Plugins
- Any agent reads from and writes to Synerise
- Developers deploy from Claude Code, Cursor, Codex
- No vendor lock-in as your stack evolves
Revenue, productivity and interoperability don't run in parallel. They multiply. Each vector amplifies the next, and the third lets the first two scale beyond Synerise.
Productivity Growth
Synerise System Agent
Creating end-to-end use cases
Workflow Agent
Building workflows from natural language, suggesting next nodes, generating descriptions, error fixer
Content Agent
Communication content, images, banner texts, slogans
JS Generator
Front-end code generation on demand
HTML Generator
Landing Page / In-App / Dynamic Content code
Jinjava Agent
Dynamic template expressions and logic
Campaign Agent
Campaign creator, statistics generator, use case advisor, error troubleshooter
Loyalty Agent
Auto-translation on move, loyalty program configuration
Recommendation Agent
Model creation, configuration, IQL advanced filtering
General Agent
Error troubleshooter and platform-wide assistance
Documentation Agent
Conversational access to platform documentation and guides
Analytics Agent
Natural-language exploration of metrics, segments, and trends
Data Management Agent
Schema, attribute, and data-quality operations on demand
Revenue Growth
Synerise System Agent
End-to-end revenue use case orchestration
AI Search Agent
Agentic search mode for intelligent product discovery
Interoperability
MCP Server
Expose Synerise capabilities to external AI systems via Model Context Protocol
Agent2Agent
Seamless collaboration with agents from other platforms through A2A protocol
Feed Enrichment
Automated data feed enrichment and synchronization across connected systems
External API Tooling
Wrap external APIs as agent-callable tools to extend the platform's reach
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
Deny · 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
01 — Human in the loop
Every write action waits for approval. The operator sees exactly what will be created and decides: deny, allow once or always allow.
02 — Role-based access
The agent creates assets (aggregates, expressions, segmentations) only within the role permissions of the user or operator it acts for.
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
news for the group
Wait
7 days
Profile filter
SUPER FAN level?
Thank-you note
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.
01 — The agent prepares the workflow
Audience, email, delay, profile filter and thank-you note: a complete journey in a few seconds.
02 — Data activated by agents
The FAN segment and the SUPER FAN level drive the customer journey with no manual setup.
03 — Human in the loop
The workflow lands on the canvas as a draft. The operator fills in the content, approves the assets and decides whether to launch it.
Development Direction
Where We're Heading
From guiding principles and meta-agent architecture to the roadmap ahead — these are the key pillars shaping the strategic direction and next generation of Synerise System Agent.
Intent-Driven Operations
Users express goals in natural language. The agent translates intent into executable actions across the entire Synerise platform — no manual configuration needed.
Natural Language to Action
Every platform capability is accessible through conversation. From building segments to launching campaigns, the agent bridges human intent and system execution.
Agent Orchestration
A meta-agent architecture coordinates specialized agents, routing tasks to the right expert while maintaining context and guardrails across the conversation.
The Synerise System Agent Architecture
Synerise System Agent functions as a Productivity OS — a single conversational interface backed by system prompts, guardrails, a memory system, and rich data contexts that routes user intent to specialized agents.
Intelligence Agent
A dedicated agent that continuously learns from platform-wide signals — user behavior, campaign outcomes, conversion patterns — to surface proactive insights, detect anomalies, and recommend next-best actions before they are requested.
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.