Miro AI, WorkingAgents, and the Enterprise Agent Landscape: Where the Pieces Fit

By James Aspinwall, co-written by Alfred Pennyworth (my trusted AI) – March 7, 2026, 06:47

The AI agent ecosystem is fragmenting into layers: visual collaboration, orchestration frameworks, governance platforms, LLM routing, protocol standards, and enterprise suites. No single product covers the full stack. This article examines Miro AI in detail, compares it to the WorkingAgents Orchestrator, maps the broader landscape – particularly products exhibited at Nvidia GTC – and identifies where complementary partnerships and service opportunities exist.

Part 1: What Is Miro AI?

From Whiteboard to AI Innovation Workspace

Miro began as RealtimeBoard – a digital whiteboard for remote collaboration. It has since grown to 100 million users across 250,000+ organizations, including 99% of the Fortune 100. In October 2025 at their Canvas 25 event in New York, Miro rebranded itself as an “AI Innovation Workspace” and unveiled a suite of AI-native capabilities. The company raised $400M in Series D at a $17.5 billion valuation, with estimated $500M+ in annual recurring revenue.

Miro is no longer just sticky notes on an infinite canvas. It is now a platform where AI agents collaborate with humans directly on visual artifacts – diagrams, prototypes, roadmaps, and specifications.

Core AI Features

AI Sidekicks are conversational agents embedded in the canvas. They see whatever you select – sticky notes, diagrams, documents – and use that context to provide feedback, generate artifacts, and suggest next steps. Pre-built Sidekicks handle common workflows: project kickoffs, competitive analysis, campaign planning. Custom Sidekicks can be configured with brand guidelines, strategic frameworks, and domain-specific methodologies.

AI Flows are multi-step, repeatable AI workflows that live directly on the canvas. Each step can be edited, models can be swapped, and prompts refined collaboratively. A Flow might convert customer interview notes into sprint plans, or transform brainstorms into scenario analyses. No code required – connect context and run.

Your AI & Knowledge is the enterprise integration layer. Teams choose between OpenAI, Anthropic, or Google Gemini hosted on AWS, Azure, or GCP. Knowledge sources connect to Glean, Amazon Q, Gemini Enterprise, and Microsoft Copilot, allowing AI to search internal company information without leaving Miro. Admins control which models and knowledge sources are available.

Miro Specs packages PRDs, prototypes, and technical context into comprehensive technical specifications that flow directly into AI coding tools via MCP – creating a canvas-to-code pipeline.

MCP Server (Public Beta, February 2026)

This is Miro’s most strategically significant recent move. Their official MCP server makes board content – architecture diagrams, PRDs, user flows, API specs – accessible to AI coding agents. It supports Claude Code, GitHub Copilot, AWS Kiro, Gemini CLI, OpenAI Codex, Cursor, Windsurf, Replit, Lovable, VS Code, Devin, and ServiceNow (coming soon).

The MCP server enables two directions of flow:

Miro reports reducing onboarding time from ~3 days to ~30 minutes for architecture understanding via this integration.

Enterprise Governance

Miro’s enterprise features are substantial:

Most notably, Miro published a formal Agent & Automation Development Lifecycle (AADLC) – a 5-stage governance framework for deploying AI agents at organizational scale:

  1. Request Intake – justify why an agent is needed
  2. Requirements Gathering – cross-functional teams define behavior, assign security/privacy risk scores
  3. Solution Design & Build – designed across data, intelligence, orchestration, integration, and observability layers
  4. QA & Validation – structured testing against approved designs
  5. Final Assessment & Handover – security review, residual risk formally accepted, recorded in risk registers

This framework was developed internally over six months and published as a reference for enterprises. It signals that Miro is thinking deeply about agent governance – not just building AI features.

Pricing

Plan Price AI Credits
Free $0 10/month shared
Starter $8/user/month Limited
Business $20/user/month 50/user/month
Enterprise Custom (30+ seats) 100/user/month

What Miro Is Not

Miro is a visual collaboration platform with AI capabilities. It is not:

Miro creates context. It designs workflows visually. It connects to AI tools via MCP. But it does not control what those agents do once they leave the canvas.

Part 2: WorkingAgents Orchestrator – What It Is

WorkingAgents is the governance and control layer between AI agents and the systems they interact with. Three gateways, one control plane:

The core principle: agents inherit the user’s access control. One identity, one set of rules. No separate agent permissions to manage. Every action has a paper trail. Every permission is controlled. Every decision is auditable.

The Orchestrator is built in Elixir on the BEAM VM – designed for high concurrency, fault tolerance, and real-time supervision of long-running agent processes. It implements:

What WorkingAgents Is Not

WorkingAgents is not:

WorkingAgents enforces governance at runtime. It doesn’t help you design the workflow – it ensures the workflow executes within defined boundaries once it’s running.

Part 3: Miro vs. WorkingAgents – Complementary, Not Competitive

These products operate at different layers of the same stack. Here’s where each sits:

Layer 5: Visual Design & Collaboration    [MIRO]
Layer 4: Workflow Design & Specification   [MIRO + UiPath Maestro]
Layer 3: Agent Orchestration Runtime       [LangGraph, CrewAI, AutoGen]
Layer 2: Governance & Control Plane        [WORKINGAGENTS]
Layer 1: LLM Infrastructure               [NVIDIA, Cloud Providers]

The Gap Between Design and Execution

Miro excels at Layer 5 and 4: teams visually design agent workflows, define specifications, create architecture diagrams, and prototype how agents should behave. Miro’s MCP server makes that context available to AI coding tools.

WorkingAgents operates at Layer 2: once agents are deployed, it controls what they can access, which LLMs they use, what tools they can call, and maintains a complete audit trail of every action.

The gap is in Layer 3: the orchestration runtime that takes a designed workflow and executes it with multiple coordinating agents. This is where frameworks like LangGraph, CrewAI, and AutoGen operate.

Where Synergy Exists

1. Miro designs the workflow, WorkingAgents enforces it

A product team uses Miro to map out an agent workflow: “Customer emails support -> Agent triages -> Agent queries CRM -> Agent drafts response -> Human reviews -> Agent sends.” This visual specification, exported via MCP, could feed directly into WorkingAgents’ workflow engine, where each step is permission-gated and audited.

2. Miro provides context, WorkingAgents provides governance

Miro’s MCP server exposes board content to AI tools. WorkingAgents’ MCP server exposes business tools (tasks, CRM, communications) with permission enforcement. An agent could read a Miro board for context (via Miro’s MCP) and then take action through WorkingAgents’ MCP – with the action gated by the user’s permissions.

3. AADLC meets runtime enforcement

Miro’s AADLC governance framework defines how agents should be approved and deployed. WorkingAgents provides the runtime enforcement layer that implements those governance decisions: which agents get which permissions, what tools they can access, what models they can use.

4. Complementary service offering

An AI consulting firm could offer:

Integration Architecture

[Miro Canvas] --MCP--> [AI Coding Tool] --generates--> [Agent Code]
                                                            |
[Miro Specs] --MCP--> [WorkingAgents Config]                |
                            |                               |
                     [Permission Rules]                     |
                     [LLM Routing Rules]                    |
                     [Audit Requirements]                   |
                            |                               |
                    [WorkingAgents Runtime] <--- deploys --- +
                            |
                     [MCP Tool Gateway]
                     [A2A Agent Gateway]
                     [LLM Router]
                            |
              [Business Systems, APIs, Data]

Part 4: The Broader Landscape – Nvidia GTC and Beyond

NVIDIA’s Agent Stack (GTC 2025-2026)

NVIDIA has positioned itself as the infrastructure layer for the entire agentic AI ecosystem:

AgentIQ (now NeMo Agent Toolkit) is NVIDIA’s open-source library for connecting, evaluating, and optimizing teams of AI agents. It is framework-agnostic – works across LangChain, CrewAI, and custom implementations. Critically, it includes native MCP integration as both client and server. This is the most directly comparable product to WorkingAgents’ orchestration capabilities, though it focuses on agent coordination and metrics rather than enterprise governance and permissions.

NVIDIA Dynamo is a full-stack open-source orchestration layer for GPU resource allocation and inference workload optimization – the infrastructure beneath the agent layer.

Llama Nemotron provides open reasoning models designed as foundations for agentic AI platforms.

AI Blueprints are pre-built reference architectures for deploying agentic AI in production – templates, not products, but they define the patterns enterprises follow.

GTC 2025 featured nearly 400 exhibitors with dedicated pavilions for agentic AI. Notable exhibitors included Dify.AI (visual agent builder), LangChain (enterprise deployment lessons), and panels with Meta, Microsoft, ServiceNow, and Accenture on enterprise agent success.

GTC 2026 (March 16-19) continues the agentic theme with confirmed participation from Adobe, Canva, CodeRabbit, Cohere, Decagon, Google DeepMind, Hugging Face, IBM Research, Meta, Microsoft, OpenAI, Shopify, Siemens, Tesla, Together AI, and Uber. Sessions specifically address agent governance: NVIDIA’s CSO David Reber on safely harnessing agentic AI, and Capital One’s Prem Natarajan on balancing innovation with governance.

Agent Orchestration Frameworks

The framework landscape has consolidated into three primary approaches:

LangGraph (LangChain) takes a graph-based approach. Agents are nodes, edges define control flow, enabling conditional logic, loops, state persistence, and parallel execution. 47M+ PyPI downloads. 30-40% lower latency in benchmarks. The most battle-tested for production stateful systems.

CrewAI uses role-based agent teams. Each agent gets a role, goal, and backstory, then crews execute tasks collaboratively. Fastest-growing for multi-agent use cases. Added A2A protocol support in 2026. Pricing from free open-source to $120K/year enterprise.

AutoGen / Microsoft Agent Framework treats workflows as conversations between agents. Microsoft has merged AutoGen with Semantic Kernel into a unified Microsoft Agent Framework targeting Azure-native deployments with built-in governance, set for GA Q1 2026.

Haystack (deepset) focuses on modular pipelines with explicit control over retrieval, routing, memory, and generation. Model-agnostic. Hayhooks can expose any pipeline as a REST API or MCP server.

DSPy (Stanford) is fundamentally different – you program, not prompt, language models. Define input/output behavior as signatures, DSPy compiles your declarations into optimized prompts and weights. No hand-crafted prompt engineering.

Framework MCP Support A2A Support Best For
LangGraph No native No native Complex stateful workflows
CrewAI No native Yes Multi-agent role-based teams
AutoGen/MS Agent No native No native Conversational agent patterns
Haystack Via Hayhooks No native Modular RAG/retrieval pipelines
DSPy No native No native Optimized LLM programming
NVIDIA AgentIQ Yes (client+server) No native Framework-agnostic coordination

WorkingAgents’ position: native MCP server with 60+ permission-gated tools, multi-provider LLM routing, and audit trail. None of these frameworks provide enterprise governance at the level WorkingAgents implements. They orchestrate agents but don’t enforce who can do what.

Enterprise AI Governance Platforms

This category is emerging rapidly, driven by the EU AI Act’s high-risk provisions taking effect in August 2026.

Zenity is purpose-built security and governance for AI agents. Runtime monitoring, prompt injection detection, over-permission flagging. Named Gartner Cool Vendor in Agentic AI TRiSM (2025). Zenity monitors agents across SaaS, cloud, and endpoints – it discovers and governs agents regardless of where they run.

FireTail provides AI audit trails: logs every AI interaction (user, time, model, prompt, response), discovers shadow AI across cloud providers, enforces policy with OWASP pre-built rules, and handles PII-aware data deletion with audit retention. This is the closest product to WorkingAgents’ audit philosophy – every action has a paper trail.

Arthur AI released the first Agentic Discovery & Governance (ADG) platform in late 2025, monitoring over 1 billion tokens across production deployments. Automated discovery of agents across compute environments, acceptable use policies, PII/PHI/IP safeguarding. Integrates with Google Vertex, AWS Bedrock, and Microsoft Agent Foundry.

Galileo AI provides modular evaluation with built-in guardrails and real-time safety monitoring. Proprietary Luna evaluation models run with sub-200ms latency. Focuses on prevention – stopping harmful outputs before they reach users.

Patronus AI focuses on automated evaluation and security. Their Percival product is an AI agent debugger that detects 20+ failure modes in agentic traces and suggests optimizations.

Guardrails AI is open-source (Apache 2.0) with 100+ community-contributed validators covering toxicity, PII, factual grounding. Near-zero latency impact (10-50ms per validator). The Guardrails Hub is a marketplace of reusable validators.

Fortanix is exhibiting confidential AI at GTC 2026 – hardware-level encryption for AI workloads, securing the data layer beneath agent operations.

Product Focus Approach
Zenity Agent security Runtime monitoring, shadow AI detection
FireTail Audit trails Log every AI interaction, policy enforcement
Arthur AI Agentic discovery Find and govern agents across environments
Galileo Output safety Real-time guardrails, prevention-first
Patronus Evaluation Post-hoc analysis, agent debugging
Guardrails AI Validation Open-source validator pipeline
Fortanix Data security Confidential AI, hardware encryption
WorkingAgents Governance + routing Permission-scoped MCP tools, LLM routing, audit trail, access control

WorkingAgents differs from all of these in a critical way: it is not a monitoring/evaluation layer bolted onto existing agents. It is the gateway through which agents access tools and LLMs. Governance is structural, not observational. You don’t detect over-permission after the fact – you prevent it by design because agents inherit the user’s access control.

Protocol Standards: MCP and A2A

MCP (Model Context Protocol) has won the tool-integration standard. Created by Anthropic, open-sourced November 2024, donated to the Linux Foundation’s Agentic AI Foundation (AAIF) in December 2025. Co-founded by Anthropic, Block, and OpenAI. Platinum members: AWS, Google, Microsoft, Bloomberg, Cloudflare.

The numbers: 97 million monthly SDK downloads, 5,800+ MCP servers, 300+ MCP clients. Supported by Claude, ChatGPT Desktop, Cursor, GitHub Copilot, Gemini, VS Code, Zed, Sourcegraph. 50+ enterprise partners including Salesforce, ServiceNow, Workday, Accenture, Deloitte.

A2A (Agent-to-Agent Protocol) was introduced by Google in April 2025 for inter-agent communication. While MCP handles agent-to-tool connections (vertical), A2A handles agent-to-agent coordination (horizontal). Built on HTTP + SSE + JSON-RPC 2.0. Agent Cards serve as metadata documents describing capabilities. Tasks progress through lifecycle states. 100+ technology companies supporting. Now under Linux Foundation governance.

MCP and A2A are complementary:

  1. User submits a request to an orchestrating agent
  2. The orchestrator uses A2A to delegate subtasks to specialized agents
  3. Those specialized agents use MCP to invoke tools, fetch documents, run computations
  4. Results return as A2A artifacts

WorkingAgents implements MCP natively (60+ tools) and positions itself for A2A as the governance gateway between agents. This is exactly the control plane that enterprises need when hundreds of agents communicate with each other and with business systems.

Enterprise Platform Players

The hyperscalers are building agent orchestration into their core platforms:

Salesforce Agentforce has 8,000+ customers as of early 2026. CRM-native autonomous agents for customer-facing workflows. Agentforce 360 with Flex Credits pricing at $0.10 per action – consumption-based, not per-seat.

Microsoft Copilot Studio / Agent 365 provides a centralized control plane for agent management. GPT-5 integration, Employee Self-Service Agent, computer use capabilities. Azure AI Foundry is the primary environment for building multi-agent systems, with security and governance as core differentiators.

ServiceNow AI Agents ranked #1 for Building and Managing AI Agents in the 2025 Gartner Critical Capabilities report. AI Agent Orchestrator for multi-agent coordination. AI Control Tower for governance and monitoring. Founding partner of the A2A protocol. Acquired Moveworks in March 2025 for autonomous task execution.

Google Vertex AI Agent Builder offers managed Agent Engine, Agent Development Kit, and Agent Garden (prebuilt sample agents). Agent Designer is a low-code visual designer. Enterprises can publish custom agents to Gemini Enterprise with controlled sharing and centralized governance.

Anthropic Co-work Plugins launched February 2026 with private plugin marketplaces for enterprises. Pre-built templates for HR, design, engineering, operations, finance. Peter Diamandis’ Moonshots podcast called these “absurdly simple – just MCP wrappers and text files” that nonetheless carved $1.5 trillion off SaaS market caps.

OpenAI Agent Store replaced the GPT Store in January 2026, pivoting from chatbot marketplace to paid agent marketplace. Agentic Commerce Protocol enables buying directly from merchants in chat.

Visual AI Workflow Builders

These occupy the space between Miro’s design canvas and production agent runtimes:

Dify.AI is an open-source LLMOps platform with visual workflow orchestration, RAG pipelines, and Backend-as-a-Service. Exhibited at GTC 2025 (Booth #3226) with NVIDIA NIM integration and live agent-building demos.

n8n is open-source workflow automation with the broadest capabilities: branching, error paths, schedules, webhooks, run logs. The power-user tool for connecting AI to everything.

Flowise is open-source, LangChain-based, best for rapid chatbot and RAG development with short feedback loops for prompt tuning.

Rivet (by Ironclad) is a node-based editor for designing, debugging, and collaborating on LLM prompt chains and agent workflows. Best visual debugging of complex prompt chains.

UiPath Maestro deserves special mention: BPMN-based visual workflow modeling that coordinates AI agents + RPA bots + humans. 30 industry templates. Live instance supervision (pause/resume/retry). This is the closest product to bridging visual design and agent orchestration runtime – but it’s workflow-modeling software, not a collaborative whiteboard.

Emerging Players Worth Watching

Tess AI raised $5M seed in March 2026. Multi-model orchestration across 250+ models with a seatless pay-for-impact pricing model. 600K+ agent tasks per month. The “no seats, just results” model is a direct challenge to per-user pricing.

Beam AI offers a modular Agent Operating System for end-to-end workflow automation with built-in governance and oversight. Multi-agent intelligence at the platform level.

Decagon (confirmed at GTC 2026) builds customer support AI agents that handle complex multi-step resolutions.

CodeRabbit (confirmed at GTC 2026) provides AI-powered code review agents.

Genspark (confirmed at GTC 2026) builds AI agent experiences for consumer search and task completion.

Part 5: Where WorkingAgents Fits – Service Opportunities

The Unfilled Position

Looking at the landscape, there is a clear gap:

What Exists What’s Missing
Visual workflow design (Miro, UiPath Maestro) Design-to-governance pipeline
Agent frameworks (LangGraph, CrewAI) Framework-agnostic permission enforcement
Governance monitoring (Zenity, Arthur, FireTail) Governance-by-design gateway
LLM APIs (OpenAI, Anthropic, Google) Unified routing with per-user controls
MCP tools (5,800+ servers) Permission-scoped MCP tool access
Enterprise platforms (Salesforce, ServiceNow) Platform-agnostic governance layer

WorkingAgents occupies the “governance-by-design gateway” position – not monitoring agents after they act, but controlling what they can do before they act. This is structurally different from every governance product in the market.

Complementary Service Models

1. Miro + WorkingAgents: Design-to-Deploy Governance Pipeline

Package: help enterprises go from “we drew our agent workflow on a Miro board” to “agents are running in production with full governance.” Miro provides the collaborative design surface. WorkingAgents provides the runtime control plane. The consultant provides the bridge.

2. Framework-Agnostic Governance Layer

Enterprises using LangGraph, CrewAI, or custom frameworks need governance regardless of framework choice. WorkingAgents’ MCP gateway could sit beneath any framework, providing permission-scoped tool access and audit trails. NVIDIA’s AgentIQ already supports framework-agnostic coordination – WorkingAgents would add the governance dimension AgentIQ lacks.

3. EU AI Act Compliance

With the EU AI Act’s high-risk provisions effective August 2026, enterprises need documented governance frameworks, risk assessments, and audit-ready compliance. WorkingAgents’ permission system, audit trail, and access control architecture map directly to these requirements. Products like trail-ml handle documentation and classification, but WorkingAgents handles runtime enforcement.

4. Hyperscaler Complement

Salesforce Agentforce, Microsoft Copilot Studio, and ServiceNow AI Agents all build governance into their platforms – but only for agents running within their platforms. Enterprises running agents across multiple platforms need a cross-platform governance layer. WorkingAgents can serve as the unified control plane.

5. MCP Tool Marketplace for Enterprises

With 5,800+ MCP servers available, enterprises need a way to expose internal tools via MCP with proper governance. WorkingAgents already does this – 60+ tools with permission gating. The service opportunity: help enterprises wrap their internal systems as MCP tools within a governed WorkingAgents environment.

Partnership Targets

Partner Synergy
Miro Visual design layer feeding WorkingAgents governance config
NVIDIA AgentIQ Framework coordination + WorkingAgents governance
Dify.AI Visual agent builder + WorkingAgents permission enforcement
n8n Workflow automation + WorkingAgents audit trail
FireTail Combined audit capability – FireTail for shadow AI discovery, WorkingAgents for governed AI
Zenity Zenity monitors what happened, WorkingAgents controls what can happen
UiPath Maestro BPMN workflow design + WorkingAgents runtime governance
CrewAI Multi-agent crews executing through WorkingAgents’ permission-scoped MCP gateway

Competitive Threats

The enterprise platform players (Salesforce, Microsoft, ServiceNow) are building governance into their agent platforms. This is the bundling threat – the same dynamic Miro faces with Microsoft Whiteboard. WorkingAgents’ defense is the same as Miro’s: go deeper on the specific capability (governance-by-design), stay platform-agnostic, and move faster than the incumbents.

The dedicated governance startups (Zenity, Arthur, FireTail) are monitoring-first, not gateway-first. They observe and report. WorkingAgents prevents. These could become partners rather than competitors.

Part 6: Market Context

The autonomous AI agent market is estimated at $8.5 billion in 2026, potentially $35-45 billion by 2030 (Deloitte). 75% of companies may invest in agentic AI by end of 2026. 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025 (Gartner).

The key differentiator is no longer building agents – it is scaling and orchestrating them with governance. As Alex Finn said on the Moonshots podcast: “At some point, the circular economy becomes indistinguishable from the real economy.” The same applies to agent governance: at some point, the governance layer becomes indistinguishable from the agent infrastructure itself.

WorkingAgents is positioned at that convergence point. The question is execution speed. The regulatory window (EU AI Act August 2026), the enterprise adoption wave (75% investing by year-end), and the protocol standardization (MCP at 97M downloads, A2A at 100+ supporters) are all creating urgency.

The pieces are on the board. The enterprises need governance. The frameworks need permission enforcement. The protocols are standardized. The visual design tools are mature. What’s missing is the runtime governance gateway that ties it all together.

That’s what WorkingAgents is building.

Sources