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UnaGo - AI Operations PlatformUnaGo
Platform features

The operating layer for AI teams that execute.

UnaGo combines agent orgs, multi-step orchestration, semantic storage, MCP tools, live browser execution, learning loops, and cost-aware model routing into one platform for real work.

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Platform power

More than chat. An AI operating system.

The useful leap is not a better text box. It is AI agents with roles, memory, tools, processes, observability, and control.

Multi-agent orchestration

UnaGo decomposes complex outcomes into agent-owned tasks, dependencies, tool calls, reviews, and deliverables.

Planner agentsDependency graphsAgent handoffs

Team orgs and roles

Build AI teams with specialist roles, shared goals, scoped permissions, and escalation paths that mirror real operations.

Agent teamsRole boundariesHuman checkpoints

Parallel and serial processes

Run research, creative, data, and QA in parallel while sensitive approvals and publishing steps stay ordered.

Parallel lanesSerial gatesReusable blueprints

Storage and semantic search

Agents work with files, smart metadata, embeddings, prior outputs, brand context, and searchable company knowledge.

Smart filesSemantic retrievalPersistent context

Server computer and browser streaming

When APIs stop short, agents can use isolated browser or computer sessions that stream live for review and takeover.

Hosted sessionsLive viewAudit evidence

Self-learning improvement loops

Successful work becomes reusable process knowledge, while failures can feed reflection, evaluation, and better future runs.

ReflectionsQuality loopsProcess memory

Cost-aware atomic agents

Small, focused agents can use the right model and tool for each step instead of pushing every task through one expensive model.

Model routingUsage trackingBudget controls

Open tool ecosystem

MCP servers connect models, media tools, ads platforms, CRMs, research sources, files, internal APIs, and local tools.

MCP toolsCustom APIsLocal bridge

Execution model

Serial where it matters. Parallel where it wins.

UnaGo can run independent agent work at the same time, then pause for ordered approval, publishing, or customer-impacting steps.

Plan the graph

The orchestrator turns a business outcome into tasks, dependencies, budgets, approvals, and success criteria.

Break down work
Assign owners
Set gates

Run work in parallel

Specialist agents move at the same time where tasks are independent, sharing context as new information lands.

Research markets
Draft assets
Prepare data

Control serial steps

Approvals, production actions, customer data access, and publishing can be sequenced with human review.

Approve spend
Verify outputs
Publish safely

Learn from delivery

Outputs, tool traces, costs, decisions, and feedback become material for future workflows and reusable playbooks.

Store context
Evaluate quality
Improve next run

Core feature set

Everything your AI team needs to operate.

From the first instruction to the final audit trail, UnaGo is built around operational work rather than one-off prompt responses.

Specialist AI agents

Marketing, media, research, sales, coding, and operations agents coordinate around outcomes instead of isolated prompts.

  • Dedicated roles
  • Progress tracking
  • Cross-agent handoffs

1,000+ MCP tools

Production tools for ads, CRM, scraping, content generation, video, voice, documents, files, and internal systems.

  • Open protocol
  • Tool catalog
  • Custom servers

Natural language command

Teams brief, review, redirect, approve, and receive final outputs from one conversation.

  • Plain-English briefs
  • Live updates
  • Human feedback

Computer execution

Agents can operate server-side runtimes and browser sessions for workflows that require real interfaces.

  • Browser control
  • Screenshots
  • Human takeover

Audit and observability

See what agents did, which tools they used, what they produced, and where approvals happened.

  • Tool traces
  • Usage records
  • Approval history

Developer extensibility

REST APIs, webhooks, event streams, and MCP development patterns make custom integrations practical.

  • REST APIs
  • Webhooks
  • Event streams

Deep dives

Explore the feature layers in detail

Start with orchestration, then look at the platform engines that make the agent team useful in production.

Multi-agent orchestration

See how one request becomes a dependency graph across specialist agents, tools, approvals, and final delivery.

Explore orchestration

Core capabilities

Tour the engines behind chat, MCP tools, workflows, storage, security, APIs, and operational execution.

View capabilities

Why it compounds

Built for outcomes, not isolated AI tricks.

The platform gets more useful as teams connect tools, store context, improve workflows, and let atomic agents handle the right parts of each process.

See pricing View integrations
Coordinate specialist agents in one workspace
Run serial and parallel work from reusable process blueprints
Search files, context, and prior outputs semantically
Stream hosted browser and computer sessions live
Route atomic tasks to cost-efficient models and tools
Record usage, approvals, tool calls, outputs, and learning signals