Why A2A Could Unlock the Next Wave of Enterprise AI Adoption
A2A could turn isolated AI agents into a composable enterprise workforce. The value lies in lower integration costs, broader choice, and end-to-end execution.
Tag
AI Agents
16 articles on this topic.
The Governance Problem in Multi-Agent Systems
Multi-agent systems distribute responsibility across a network. Governed execution keeps authority, evidence, approvals, and accountability intact as work moves.
How an AI agent for invoice processing keeps a human in control
Invoice processing is high volume, rule-bound, and unforgiving of errors. A governed AI agent can read, match, and code invoices while routing every payment decision to a person.
Governed AI for small business: automation lean teams can trust
Small teams have the most to gain from AI agents and the least room for a mistake. Governed AI gives a lean business the leverage of automation without giving up oversight.
How an AI agent for expense reports keeps approvals in place
Expense reports are repetitive, rule-bound, and easy to get wrong. A governed AI agent can read receipts, apply policy, and route the judgment calls to a human, while recording every step.
Designing a multiplayer AI workspace for human teams and agents
Adding an assistant to a chat window does not make a workspace where humans and AI agents work together. Six design decisions do, and most of them are about control rather than capability.
Are AI agents safe for business? What governance actually changes
The honest answer to whether AI agents are safe is: only when they are governed. Scope, approvals, and audit trails are what turn an autonomous agent into one you can trust.
AI agents for customer support that act, with guardrails
Support is more than answering questions. A governed AI agent can triage, draft, and resolve routine tickets while keeping a person on refunds, escalations, and anything sensitive.
AI agent collaboration platform: how to evaluate multiplayer AI
Three very different products are sold under the same name. Only one of them is somewhere a team can actually work, and six questions tell you which is which.
Multiplayer AI: what changes when a whole team works with agents
Google Docs beat Word by going multiplayer. Figma beat Photoshop. AI has not had that moment yet, and the missing piece is not shared context. It is shared control.
Giving AI agents identity without letting them impersonate humans
How to design agent identity, delegation, authorization, and credentials without turning automation into invisible impersonation.
Keeping agent autonomy inside the lines: a technical look at governed execution
Open protocols are solving how agents reach tools. The harder problem is deciding what an agent may do, proving what it did, and stopping it mid-run. Here is how we think about that.
Approval gates are a systems primitive, not a button
The engineering behind approval gates: suspension, evidence, expiry, revalidation, revocation, and safe continuation.
Chatbots assist, copilots suggest, RPA breaks: what actually moves work
Different AI and automation categories solve different parts of the work problem. The missing category is the governed AI agent that can plan, pause, execute, and produce evidence.
Multiplayer AI is a distributed systems problem
A principal architect’s guide to state, failures, retries, and consistency when humans and AI agents work together.
A practical readiness checklist for business AI agents
A readiness checklist for teams evaluating AI agents: start with ownership, system access, approval thresholds, audit requirements, and the operational workflows where autonomy is actually useful.