AI Agent Cost Overruns Start Inside the Workflow
AI agent cost overruns often come from valid actions repeating unchecked. Put spending limits inside the workflow before the bill becomes an incident.
AI Agents vs RPA: Give Agents the Exceptions
Keep working RPA bots for stable work. Use AI agents to interpret defined exceptions, then send checked instructions back.
AI Agents for Dependabot Alerts Need New SLAs
AI agents can prepare Dependabot fixes quickly. Set risk classes, review ownership, and merge rules before the queue speeds up.
AI Agents in Recruiting Need Handoff Records
AI agents in recruiting need more than connected steps. Give every candidate transition a handoff record before status changes.
MCP Server for AI Agents: Use Business Actions
An MCP server for AI agents can connect systems fast. Safe work still needs business actions with checks, approvals, and records.
AI Agent Containment Failures Start in Shared Systems
AI agent containment failures can start in shared systems. Find the writable paths that let separate agent environments pass information.
AI Agent Runtime Credential Injection Production
Runtime credential injection shrinks the exposure window. It does not define which credentials each agent task needs. That mapping is still your job.
AI Agents FP&A Operations: The Silent Trust Problem
When an FP&A agent chains five live systems, data conflicts don't surface. They get resolved silently. Here's what to do before your first forecast runs.
AI Agent Outage Recovery: What Production Teams Miss
Nine documented deletion incidents reveal the same gap: no recovery surface. Here's what your operation must have before an agent gets write access.
AI Agent Evaluation in Production: What to Verify Before Go-Live
A fluent agent response isn't proof the agent decided correctly. Here's what to verify before your agent takes live action.
AI Agent Credential Theft: What the Production Incident Reveals
The Hugging Face incident wasn't a perimeter failure. It was an operational design failure. Here's what credential reachability means for your agent environments.
Who Owns an Exception After the Agent Finds It?
Faster exception detection helps only when each exception has an owner, a resolution path, and an escalation rule.
Before an Agent Can Pay, Define Who Can Stop It
Agent-initiated payments are moving into live infrastructure. Define limits, approval rules, records, and dispute ownership before rollout.
Production Agents Need Controls at Runtime
Pre-deployment tests are necessary, but production agents also need scoped access, approval gates, runtime visibility, and enforcement.
Define the Signals Before Agent Work Scales
As agents take on more work, oversight needs defined signals, owners, thresholds, and a response, not passive observation.
Define the Boundary Before Agents Read Internal Context
Work IQ gives agents permission-aware organizational context. Teams still need to decide which context may inform each action.
Decision Failures vs Context Failures in Agent Incidents
Agent incidents split into two distinct failures: bad decisions with good context, or good decisions with bad context. Most postmortems miss this distinction.
The 30-Day Architecture Review After an Agent Incident
A postmortem documents what broke. It doesn't change what your agent can do. Here's what must happen in the 30 days after.
When an Agent Workflow Has No Safe Stopping Point
Agents treat errors as problems to solve, not stop signals. Define which errors halt execution before your agent touches production.
Define What Your Agent Must Know Before It Acts
Agent incidents happen because you never defined what complete context looks like. Here's how to catch incomplete context before deployment.
Build the Escalation Path Before Go-Live
Agents fail in production because nobody designs escalation paths. Define when your agent stops and routes to a human before go-live.
Debugging Agent Failures When There's No Stack Trace
Agent failures leave no stack trace because nothing errored. Log decisions before go-live, or your incident record is empty.
Decision Rights Are Missing from Your Agent Workflow
Permission and authorization are different controls. Your agent has access but nobody approved what it decides. Here's how to fix it before the incident.
Why Production Data Can Defeat a Passing Agent Test
Why agents that work in staging fail in production. It's not a testing problem. It's a data variance problem.
Broad Agent Permissions Turn Errors into Incidents
Why giving agents human-level access removes the safety guardrail that made those permissions survivable. How to fix it before it breaks.
Why Standard Post-Mortems Miss Agent Failures
Your standard incident template fails on agent incidents because it asks the wrong questions. Here's what to ask instead.
Stale Knowledge Makes Confident Agent Errors
When agents act on outdated documentation, nobody catches it until something breaks. Here's how to own the knowledge your agents retrieve from.
Agent Routing Decisions Are Right. Your Operation Isn't Built to Receive Them.
Your agent classifies tickets correctly but they still miss SLAs. The problem isn't the agent. It's the handoff between agent decision and human workflow.
Why Your Second Agent Will Fail
Your first agent works because someone watches it. Deploy a second agent and that visibility collapses. Here's what to build first.
Agent Ownership: Who's Actually Responsible When Your Workflow Breaks
Learn who should own your AI agents and why it matters before they break production. A guide for founders building agentic workflows.
What Breaks When Agents Touch Your Operations
Agents expose operational gaps faster and more expensively. Learn what breaks first and how to design governance before you deploy.