Coordinating multi-agent workflows.

A coordination layer that lets several AI agents work on the same process. Each agent has a role, calls the tools it needs, hands off state to the next agent, and pauses for human review when required. A working setup includes shared memory across agents, defined approval gates, and a record of every run.

Agents share an operational store of the decisions, approvals, preferences, and work history your org has already produced. Each step declares the tools it can call, the context it needs, and whether a human reviews the result. Agents read from that store at runtime and write back to it as they go.

Workflow automation runs a fixed sequence of pre-defined steps. Agent orchestration coordinates agents that decide what to do based on context, call tools as needed, and request approvals when uncertain. synapse supports both: deterministic steps for stable processes, agent steps where judgment is needed.

Through a shared store that any agent in the workflow can read and write during execution. In synapse, that store holds artifacts, approvals, nudges, and prior outputs. The next agent queries the store directly rather than relying on a human to copy details between threads.

Each agent only sees a slice of the work. One has the customer context, another has the product requirement, another has the QA exception. Without a shared store, a human ends up relaying details between them. With one, each agent looks up what earlier agents already produced.

Map the work into agent-sized missions, decide which steps need human approval, list the tools each agent needs, and define what state moves between them. In synapse, you describe the workflow once. The platform provisions agents with the right tool access, and human review points are stored for future runs.

CRMs (HubSpot, Attio, Salesforce), outbound (Instantly, Loops, Apollo), enrichment (Clay), communication (Slack, Gmail, LinkedIn), project tracking (Linear, Jira, GitHub), and document storage (Google Docs, Notion). Every tool call an agent makes is recorded as part of the workflow history.

Any step can declare a human review gate or a fallback path. When an agent hits a low-confidence decision, an out-of-policy action, or a missing input, the workflow pauses and surfaces the relevant context to the right person. After approval or correction, the workflow resumes and the correction is saved so the next run does not re-ask.

At the decisions that matter: approving risky actions, reviewing exceptions, and nudging agents when context changes. The routine relay work — restating background, copying details between threads — is what the agents handle.

No. Agents handle the coordination, context retrieval, and execution between judgment points. Humans still make the calls that require taste, accountability, or external knowledge. The shift is in what people spend time on, not how many people you need.

Both. It includes the surface for defining and editing workflows, and the runtime that executes them — agents, tools, approvals, and memory. The two are coupled so the workflows your team writes can actually run with the right context.

Product, design, engineering, QA, GTM, support, and operations teams — anywhere a process crosses roles and systems. Common examples include release readiness, customer escalation triage, QA reproduction, roadmap research, sales follow-up, and launch coordination.