Comparison
JieGou vs Make
Make built visual AI agents — JieGou built visual AI agents with 10-layer governance
Make (formerly Integromat) has evolved from a visual scenario builder into a visual AI agent platform. In April 2025 Make launched AI Agents — autonomous, decision-making agents that run inside Make's no-code environment, connecting to 3,000+ apps with multi-model LLM support (GPT-4, Claude, Gemini, Grok, plus OpenAI-compatible endpoints via BYOK). Maia, Make's natural-language interface, lets anyone describe an automation in plain English and get a working scenario — lowering the adoption barrier significantly. The next-generation AI Agents (announced October 2025) add a redesigned UI, reasoning panel, and multimodal inputs. Make built visual AI agents. JieGou built visual AI agents with 10-layer governance and department-first curation. Make's AI Agents have no governance layer — no compliance frameworks, no tool approval gates, no delegation safety, no audit evidence export. JieGou's governance is 10 layers deep — from identity and encryption through regulatory compliance and evidence export. For teams that need both AI agents and enterprise trust infrastructure, the difference matters.
Last updated: March 2026
The Learning Loop Advantage
Other platforms execute your instructions. JieGou learns from every execution and gets better.
Make's scenarios execute identically every run. JieGou learns — capturing knowledge, refining prompts, and surfacing insights that make every subsequent run better.
Explore the Intelligence Platform →Key Differences
| JieGou | Make | |
|---|---|---|
| Time to First Workflow | 5 minutes — pick department, install pack, run template | 30-60 minutes — design visual scenario, configure modules, map data fields |
| Department Structure | 20 curated department packs with pre-built AI workflows | No department structure — build scenarios from individual modules |
| Non-Technical Accessibility | No-code: pick department → run template → get results | Maia NL interface generates scenarios from plain English; visual builder still requires module configuration for advanced use |
| Core Design | AI-native with structured prompt/response schemas and 10-layer governance | Visual automation platform with AI Agents (April 2025) and Maia NL interface — AI now core, but no governance layer |
| Governance Layers | 10-layer governance stack (identity, encryption, data residency, RBAC, escalation, tool approval, audit, compliance timeline, evidence export, regulatory) | Enterprise Grid: 2-3 layers (centralized control, enhanced audit logs); AI Agents ship with zero governance layers |
| Agent Orchestration | Multi-agent with guardrails, cycle detection, shared memory isolation, and auto role inference | Make AI Agents with autonomous decision-making across 3,000+ apps; next-gen agents with reasoning panel and multimodal inputs — but no delegation safety, cycle detection, or memory isolation |
| Regulatory Compliance | EU AI Act 8-article mapping, NIST RFI, HIPAA/GDPR/SOX/FedRAMP presets, compliance calculator | SOC 2 compliance tracking via Enterprise Grid audit logs |
| Protocol Support | Triple-protocol: AGENTS.md + MCP + A2A | Proprietary integrations with early MCP support |
| Self-Hosted | Docker Compose starter kit + air-gapped deployment (Ollama + Redis) | No self-hosted option |
| Department Curation | 25+ department packs, 600+ templates, one-click install | Enterprise Grid centralized control; no department packs |
| LLM Integration | 9 providers with BYOM bakeoffs — structured A/B testing to prove which model works best per workflow | Multi-model: GPT-4, Claude, Gemini, Grok + OpenAI-compatible endpoints via BYOK (all paid plans); 350+ AI app connectors — but no bakeoffs or model comparison tooling |
| Structured I/O | Typed input/output schemas on every recipe | Free-form data mapping between modules |
| Approval Gates | Native pause-and-resume with email notifications | Requires external webhook workarounds |
| Knowledge Sources | 12 enterprise knowledge sources (Coveo, Glean, Elasticsearch, Algolia, Pinecone, Vectara, Confluence, Notion, Google Drive, OneDrive/SharePoint, Zendesk, Guru) + built-in RAG | No enterprise knowledge integration; 3,000+ app connectors for data syncing only |
| AI Evaluation | AI Bakeoffs with multi-judge scoring | No built-in AI quality testing |
| Org-Wide Visibility | Operations Hub: Automation Landscape Map, Governance Dashboard, Org Analytics with executive summaries — all organized by department | Make Grid: org-wide automation landscape visualization + Enterprise Grid centralized control |
| Pricing | Free tier + $49/mo Pro (BYOK LLM costs separate) | Credit-based: from $10.59/mo; AI-intensive actions cost multiple credits; BYOK on all paid plans; Enterprise Grid for large deployments |
| Quality Assurance | Continuous LLM-judge scoring + statistical AI Bakeoffs + nightly simulation testing with adversarial inputs | Enterprise Grid AI-assisted debugging for scenario logic |
| Integrations | MCP-native: 250+ integrations where AI discovers and uses tools via open protocol | 3,000+ app modules with visual data mapping + 350+ AI app connectors; early MCP support |
| Multi-Agent Safety | Delegation cycle detection, shared memory isolation, auto role inference — built-in guardrails | AI Agents with reasoning transparency and real-time decision-making; no delegation safety primitives, no cycle detection, no memory isolation |
| Visual Canvas | DAG builder with agent-aware nodes, memory overlays, cycle detection | Best-in-class visual scenario builder for data mapping |
| Test Coverage | 13,320+ tests with 99.1% code coverage; nightly regression suites | No published test suite or coverage metrics |
| Hybrid Deployment | VPC execution agents + Docker Compose air-gapped option (Enterprise) | Cloud-only SaaS; no on-premise option |
| Data Residency | Configurable data residency with compliance presets | EU and US data centers available |
| Evidence Export | 17 TSC controls, 8 evidence categories, auditor-ready PDF/JSON export | Enhanced audit logs (Enterprise Grid) |
| A2A Protocol | Agent-to-Agent protocol for cross-platform interoperability | No A2A; early MCP support for tool discovery |
| Agent Threat Detection | 4 inline detectors: prompt injection, data exfiltration, privilege escalation, resource abuse — runs during execution | No agent-level threat detection |
| AI Agent Architecture | Department-scoped agents with graduated autonomy (4 levels), GovernanceScore, tool approval gates, and compliance framework mapping | Make AI Agents (April 2025): autonomous decision-making with NL goals, context-aware adaptation, reasoning panel — but zero governance layers, no autonomy controls, no compliance mapping |
| Natural Language Interface | Conversational AI agent builds workflows from plain English + 25+ department packs for instant start | Maia: NL-to-scenario builder that generates full automations from descriptions — available on all plans including free tier |
| AI Agent Governance | 10-layer governance on every agent: identity, encryption, data residency, RBAC, escalation, tool approval, audit, compliance timeline, evidence export, regulatory | No agent governance — AI Agents run with full autonomy, no tool approval gates, no compliance controls, no audit evidence export |
Why Teams Choose JieGou
Structured AI outputs
Every recipe enforces typed input and output schemas, so downstream steps always receive consistent, machine-readable data from the LLM.
Knowledge bases for context
Upload documents and build RAG-powered knowledge bases that give recipes domain-specific context — no external vector DB required.
AI Bakeoff evaluation
Compare model performance with statistical rigor using multi-judge scoring, synthetic inputs, and confidence intervals.
Brand voice governance
Set organization-wide brand voice guidelines that are automatically applied to every AI-generated output.
Governed AI agents vs. ungoverned AI agents
Make launched AI Agents — but with zero governance. JieGou wraps every agent in 10 governance layers, 4 autonomy levels, tool approval gates, and compliance framework mapping. Same capability, fundamentally different trust posture.
When to Choose Each
Choose JieGou when you need
- AI-first workflows with structured LLM reasoning
- Teams needing built-in knowledge bases for AI context
- Processes requiring human approval gates
- Organizations evaluating and comparing AI model quality
Choose Make when you need
- Complex data transformation between APIs
- Visual scenario building with advanced data mapping
- Teams needing extensive API connector library
- Error handling with advanced routing and retry logic
What Make Does Well
Make AI Agents with autonomous decision-making
AI Agents (April 2025) bring autonomous, context-aware decision-making directly into Make's no-code environment. Agents use natural language to understand goals, adapt in real time, and connect to 3,000+ apps. Next-gen agents (October 2025) add a reasoning panel, redesigned UI, and multimodal inputs (documents, images, audio).
Maia natural-language automation builder
Maia lets anyone describe an automation in plain English and generates a complete working scenario — available on all plans including free. Significantly lowers the adoption barrier for non-technical users.
Multi-model LLM support
AI Agents support GPT-4, Claude, Gemini, Grok, and OpenAI-compatible endpoints. Custom AI provider connections (BYOK) available on all paid plans — not just enterprise tier.
Best-in-class visual scenario builder
Drag-and-drop visual editor with advanced data mapping, branching, and error handling that sets the standard for visual automation design.
Enterprise Grid for centralized governance
Enterprise Grid provides centralized scenario control across departments, enhanced audit logs for SOC 2 compliance, and AI-assisted debugging.
Make Grid for org-wide automation visibility
Organization-wide visualization tool that maps your entire automation landscape, showing how scenarios interconnect across departments.
3,000+ app connectors with 350+ AI apps
Extensive integration library with deep module support for popular apps, plus 350+ dedicated AI app connectors for specialized AI workflows.
MCP support for standardized tool discovery
Early adoption of Model Context Protocol enabling standardized tool discovery and integration across AI-powered scenarios.
Credit-based pricing with custom AI on all paid plans
Plans start at $10.59/mo with credit-based billing. Custom AI provider connections available on all paid tiers. AI-intensive actions cost multiple credits.
Frequently Asked Questions
Can JieGou replace Make for all my automations?
JieGou excels at AI-powered workflows. For pure data transformation between APIs without AI, Make may still be a better fit. Many teams use both — Make for data plumbing, JieGou for AI logic.
Does JieGou have a visual builder like Make?
JieGou has a workflow builder with step sequencing, but its focus is on AI recipe configuration rather than visual data mapping. The conversational AI agent can also build workflows from plain English.
How do knowledge bases work in JieGou?
Upload PDFs, Markdown, or text files. JieGou processes them into a searchable knowledge base that recipes can reference automatically via RAG, giving AI responses domain-specific context.
Can I use Make and JieGou together?
Yes. Use webhooks to trigger JieGou workflows from Make scenarios, or send JieGou outputs back to Make for downstream data routing.
Is Make cheaper than JieGou?
Make's paid plans start at $10.59/mo with credit-based billing (AI actions cost multiple credits). Custom AI provider connections (BYOK) are available on all paid plans. JieGou is $49/mo Pro with BYOK LLM costs separate. Make is typically cheaper for non-AI scenarios; JieGou provides more value for AI-heavy workflows with AI Bakeoffs, knowledge bases, governance, and department packs included.
Make now has AI Agents — how is JieGou different?
Make AI Agents (April 2025) bring autonomous decision-making into Make's visual builder with multi-model support and 3,000+ app connections. Maia lowers the barrier further with natural-language scenario creation. However, Make AI Agents have zero governance — no tool approval gates, no compliance frameworks, no delegation safety, no audit evidence export. JieGou wraps every agent in 10 governance layers, graduated autonomy (4 levels), GovernanceScore quantification, and three regulatory framework mappings (EU AI Act, NIST AI RMF, ISO 42001). Make built the agent. JieGou built the trust infrastructure around the agent.
What is Maia and how does it compare to JieGou's conversational agent?
Maia is Make's natural-language interface that generates full scenarios from plain English descriptions — available on all plans including free. JieGou's conversational AI agent also builds workflows from natural language. The difference: JieGou pairs NL building with 25+ department packs, so teams get pre-built, governance-wrapped workflows instead of starting from scratch. Maia builds scenarios; JieGou gives you a curated starting point with governance built in.
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Industry data: 34% of enterprises rank security & governance as their #1 priority when choosing an AI agent platform.
of enterprises cite security & governance as #1 priority
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