Enterprise Automation
AI Agents in a Customised ERP: Get the Change Request Right
Follow a supplier change request through custom fields, related records, and approval rules, then verify exactly what the ERP has saved and submitted.
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Writing for production agents.
Practical writing on system access and the infrastructure agents need to move from answers to action.
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AI strategy
- AI won't transform a company you refuse to rebuild
- How to Evaluate an Enterprise AI Pilot: A Scorecard for Completed Work
- MCP is the wrong default for enterprise AI
- Monarch Sydney Panel with BCG X and Airtree: When AI Moves Faster Than the Enterprise
- The map your AI needs is already inside your systems
- Who Owns an AI Workflow After the Pilot?
- Your AI Strategy Won’t Work If Everyone’s Job Stays the Same
- Your next software builder might work in finance
Application access
- AI Agent Integration Platforms: How to Choose
- Build With Claude or OpenAI Directly, or Add Monarch?
- Computer Use or Monarch for Enterprise Workflows?
- Connecting AI Agents to Internal Apps Without an MCP Server
- How AI Agents Work With Legacy Systems Without a Suitable Public API
- Mainframes Aren’t Dinosaurs
- Monarch vs Arcade for Enterprise AI Agents
- Monarch vs CData Connect AI for Enterprise Agents
- Monarch vs Composio for Enterprise AI Agents
- Monarch vs Google Gemini: what your Google stack covers
- Monarch vs Microsoft Copilot, Copilot Studio, Foundry, and Fabric
- Monarch vs Workato for enterprise AI agent integration
- Why AI Agents Need an Application Map
- Your Business Can Do More Than Your AI Connectors Allow
Automation
- AI Agents for Accounts Payable: Resolve the Invoice Exception Before the Payment Run
- AI Agents for Airline Disruption: Close the Gap Between Rebooking and a Place to Stay
- AI Agents for Australian Energy Retailers: Start With Billing Investigations
- AI Agents for Employee Offboarding: Close the Gaps Across HR and IT
- AI Agents for Financial Hardship: Stop Making Customers Repeat Their Story
- AI Agents for Insurance Claims: Build the Evidence Before the Settlement
- AI Agents for Supplier Delays: Give Planners a Delivery Date They Can Use
- AI Agents for Telecommunications: Recover Orders That Fall Between CRM and Provisioning
- AI Agents in a Customised ERP: Get the Change Request Right
- Monarch vs UiPath for Enterprise AI Agents
- Workato vs MuleSoft vs Automation Anywhere vs Monarch
Governance
Reliability
Featured and recent articles
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Agent Reliability
Computer Use or Monarch for Enterprise Workflows?
Compare how agents reach application actions, what gets reused and repaired, and how each approach proves the work actually happened.
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Agent Integration
Monarch vs CData Connect AI for Enterprise Agents
Compare supported queries and writes, curated tools, and application discovery through a workflow that takes a held order to an approved release.
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Agent Integration
Connecting AI Agents to Internal Apps Without an MCP Server
Build one useful business operation, establish its permissions and completion checks, then choose how your agent will call it.
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Agent Integration
Monarch vs Arcade for Enterprise AI Agents
Compare authorised tool execution, custom application work, and deployment choices, then test the complete workflow your team needs to run.
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Agent Integration
Monarch vs Composio for Enterprise AI Agents
Compare application access, authentication, and workflow ownership, including the work needed when a required action sits outside a ready-made tool.
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Energy & Utilities
AI Agents for Australian Energy Retailers: Start With Billing Investigations
A useful energy workflow connects the complaint, account, metering evidence, and reviewer. Prove that investigation before expanding into financial actions.
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Enterprise Automation
Monarch vs UiPath for Enterprise AI Agents
Compare how Monarch and UiPath connect existing agents to enterprise systems, reuse automations, and keep control of work across applications.
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Enterprise AI
Who Owns an AI Workflow After the Pilot?
An AI workflow needs someone accountable for its results after launch, with the time, evidence, and authority to keep improving it.
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TechnicalNew
Mainframes Aren’t Dinosaurs
Alex Nauda on mainframe workloads, existing integrations, and bringing agentic workflow development to terminal applications.
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Enterprise AI
Your next software builder might work in finance
What OpenAI’s non-engineering teams suggest about enterprise work over the next three years.
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Enterprise AI
Monarch Sydney Panel with BCG X and Airtree: When AI Moves Faster Than the Enterprise
Sam Senior, Michael Schniering of BCG X, and Claire Smith on what separates the companies getting returns from AI. Full video and transcript.
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Enterprise AI
Your Business Can Do More Than Your AI Connectors Allow
A connector logo does not mean your agents can finish the work. Expect deeper application discovery, reusable execution, and people leading the business change.
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Agent Integration
Monarch vs Microsoft Copilot, Copilot Studio, Foundry, and Fabric
Microsoft's AI products serve different roles. See how Monarch discovers application operations and runs cross-system agentic workflows alongside your existing stack.
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Enterprise AI
Your AI Strategy Won’t Work If Everyone’s Job Stays the Same
AI adoption requires changes to people’s workloads, incentives, and responsibilities. Give them time to improve the work and authority to lead the agents doing it.
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Insurance
AI Agents for Insurance Claims: Build the Evidence Before the Settlement
An insurer's AI workflow should connect the policy, repair estimate, and claim history, then help the team complete the next action with the customer properly informed.
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Financial Services
AI Agents for Financial Hardship: Stop Making Customers Repeat Their Story
A useful banking agent connects the customer's request, account history, and existing support arrangement, then helps the team carry its decision into the systems that administer the loan.
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Airlines
AI Agents for Airline Disruption: Close the Gap Between Rebooking and a Place to Stay
Rebooking leaves work across hotel suppliers, ground transport, and case systems. Start with one overnight assistance workflow and verify every arrangement.
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Agent Governance
Permission-Aware Enterprise Agents: Whose Authority Applies?
Follow an agent's authority from the requesting user through connected accounts, business approval, execution, and removal of the access it creates.
Read articleEnterprise Automation
AI Agents in a Customised ERP: Get the Change Request Right
Follow a supplier change request through custom fields, related records, and approval rules, then verify exactly what the ERP has saved and submitted.
Read articleAgent Reliability
Computer Use or Monarch for Enterprise Workflows?
Compare how agents reach application actions, what gets reused and repaired, and how each approach proves the work actually happened.
Read articleAgent Integration
Monarch vs CData Connect AI for Enterprise Agents
Compare supported queries and writes, curated tools, and application discovery through a workflow that takes a held order to an approved release.
Read articleAgent Integration
Connecting AI Agents to Internal Apps Without an MCP Server
Build one useful business operation, establish its permissions and completion checks, then choose how your agent will call it.
Read articleAgent Integration
Monarch vs Arcade for Enterprise AI Agents
Compare authorised tool execution, custom application work, and deployment choices, then test the complete workflow your team needs to run.
Read articleAgent Integration
Monarch vs Composio for Enterprise AI Agents
Compare application access, authentication, and workflow ownership, including the work needed when a required action sits outside a ready-made tool.
Read articleEnergy & Utilities
AI Agents for Australian Energy Retailers: Start With Billing Investigations
A useful energy workflow connects the complaint, account, metering evidence, and reviewer. Prove that investigation before expanding into financial actions.
Read articleEnterprise Automation
Monarch vs UiPath for Enterprise AI Agents
Compare how Monarch and UiPath connect existing agents to enterprise systems, reuse automations, and keep control of work across applications.
Read articleEnterprise AI
Who Owns an AI Workflow After the Pilot?
An AI workflow needs someone accountable for its results after launch, with the time, evidence, and authority to keep improving it.
Read article
Enterprise AI
AI won't transform a company you refuse to rebuild
Most enterprises use AI to run a slightly faster version of the company they already have. The bigger move is to rebuild how it works while it keeps running at full speed.
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Telecommunications
AI Agents for Telecommunications: Recover Orders That Fall Between CRM and Provisioning
An accepted broadband order can still leave a customer without service. Recovering it means following the work through provisioning, inventory, and billing, then proving the records agree.
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HR Operations
AI Agents for Employee Offboarding: Close the Gaps Across HR and IT
Carry an approved departure through access removal, work handover, and records preservation, with evidence of what each system actually completed.
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Enterprise AI
The map your AI needs is already inside your systems
An enterprise AI ontology is the map that lets agents operate your business. Most of it already lives in your software. Discover it, don't rebuild it from memory.
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Manufacturing
AI Agents for Supplier Delays: Give Planners a Delivery Date They Can Use
Follow a late purchase-order line from a supplier's revised promise through split deliveries, production decisions, and actual receipt.
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Accounts Payable
AI Agents for Accounts Payable: Resolve the Invoice Exception Before the Payment Run
Follow an invoice discrepancy through purchasing, receipts, supplier correspondence, and AP, then verify that the approved correction reached the records that control payment.
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Enterprise AI
MCP is the wrong default for enterprise AI
MCP makes connecting agents to your systems easy — not dependable. Why it is the wrong default for enterprise workflows, and what to run instead.
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Agent Integration
Monarch vs Google Gemini: what your Google stack covers
Google already supplies models, workplace AI, connectors, and agent infrastructure. Compare the application access your workflow still needs.
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AI evaluation
How to Evaluate an Enterprise AI Pilot: A Scorecard for Completed Work
Define which cases enter the pilot, what counts as a useful result, and how you will compare it with today's operation before the first live run.
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Agent Integration
Monarch vs Workato for enterprise AI agent integration
Compare how Workato and Monarch make enterprise application operations available to AI agents, and test the maintenance work behind a complete workflow.
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Benchmarks
Same model. 23% more workflows completed.
We ran 591 AutomationBench workflows with and without Monarch. Opus 5's completion rate rose 23%; GPT-5.6 Sol and Kimi K3 improved too.
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Agent Integration
Build With Claude or OpenAI Directly, or Add Monarch?
Compare the same frontier models with existing or custom tools and with Monarch, including the application work your team must build and maintain.
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Enterprise Automation
Workato vs MuleSoft vs Automation Anywhere vs Monarch
Compare Workato, MuleSoft, Automation Anywhere, and Monarch on workflow completion, agent capabilities, total cost, and Australian deployment requirements.
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Agent Reliability
Why Enterprise AI Agents Fail and What Failed Runs Cost
Learn why enterprise AI agents fail, how to diagnose the cause, and how to calculate cost per completed workflow including retries and human intervention.
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Agent Integration
AI Agent Integration Platforms: How to Choose
Compare Workato, Monarch, Composio, and Merge Agent Handler, then separate integration needs from the choice to build with Microsoft, Google, OpenAI, or Claude.
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Legacy Systems
How AI Agents Work With Legacy Systems Without a Suitable Public API
See how AI agents can use authorised private web interfaces and UI paths in legacy systems, with an application map, approval steps, and verified results.
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System Access
Why AI Agents Need an Application Map
An application map connects records, actions, permissions, and validation checks so AI agents can execute workflows with explicit business rules.
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