agents
30 posts tagged here.
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Before widening agent authority, review reversals and overrides by commitment type
AI rollback is not just a go-or-stop decision. If an agent is creating real commitments, teams need to study what had to be reversed, what humans overrode, and which authority boundary should change next.
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Customer-facing agents are commitment systems, not chat widgets
Meta Business Agent is a useful signal because it moves customer chat agents from answering questions toward booking, selling, escalating, and acting on behalf of the business. That changes the management problem.
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The next agent platform decision is evidence portability
As agent platforms become the way work is run, the important question is not only which system can coordinate agents. It is whether the evidence of that work stays usable when the platform changes.
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An agent commitment register should sit beside the control plane
The new agent-governance stack is getting better at runtime policy, but teams still need a simple record of what their agents actually commit the business to do.
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Agent access mode is a management decision, not an implementation detail
As Microsoft Agent 365 pushes agents toward delegated access, application access, and their own identities, the practical question is not just whether an agent can reach content. It is what kind of actor the organization is choosing to create.
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An agent inventory should track authority, not just existence
The enterprise agent market is moving toward inventories, governance tools, and runtime controls. The useful question is not only which agents exist, but what each one is allowed to do.
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Agent spending limits are authority design, not a payment feature
Google I/O made the next agent boundary easier to see: when agents can act, book, buy, and coordinate across tools, spending controls become part of the operating model, not a billing afterthought.
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The agent operating review should combine the evidence, not repeat the dashboard
Progress reports, exception logs, audit packets, authority maps, and evidence ledgers only matter if they come together in one operating review that changes what the agent is allowed to do next.
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The agent evidence ledger is the missing companion to the authority map
An authority map says what an agent may do. An evidence ledger says why it has earned that authority, where trust is still provisional, and what should change after real operating evidence appears.
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A process map becomes useful for AI when it becomes an authority map
Process maps are useful before agent roadmaps, but they become much more valuable when they show where authority changes hands: what an agent may observe, prepare, recommend, execute, escalate, or never touch.
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Process maps should come before agent roadmaps
As enterprise AI shifts from pilots to production, the practical bottleneck is not choosing the next agent to build. It is understanding the workflow well enough to decide where agency, automation, review, and rollback actually belong.
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An agent exception log should change the workflow, not just judge the agent
The useful exception log is not a scorecard for the agent. It is the repair list for the workflow that produced the exception.
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An agent progress report should be a control surface, not a status update
As AI agents become normal workflow participants, their progress reports need to help managers change authority, not just feel informed.
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The agent lifecycle is an authority lifecycle
AI agents should not be managed as isolated tools. They need a lifecycle for earning, expanding, reducing, restoring, and retiring authority.
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Retiring an agent is an authority decision, not a cleanup task
When an AI agent stops earning trust, retirement should be a designed authority transition, not an informal deletion after everyone has moved on.
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Restoring agent authority should require remediation evidence
After an AI agent is demoted, authority should return because the operating evidence changed, not because enough time passed.
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Demotion criteria are part of agent authority design
If an AI agent can earn more authority, it should also have clear conditions for losing authority before failure becomes dramatic.
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The agent audit packet should exist before the next permission change
After an AI agent is deployed, do not wait for an incident to gather evidence. Build a small audit packet before changing its permissions.
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Agent promotion reviews should be operating reviews, not vibe checks
Before an AI agent gets more authority, review how it behaved in real work: exceptions, escalations, rollback evidence, and human review burden.
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The agent rollback plan should exist before the agent gets more authority
If an agent can change real work, the rollback plan is part of the authority design, not an afterthought for when something goes wrong.
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The agent exception log is more important than the success rate
Success rates tell you whether an agent works in normal cases. Exception logs tell you whether it deserves more authority.
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Before you expand an agent's authority, ask what it has earned
Agent adoption is moving faster than production trust. The practical answer is not to freeze autonomy or grant it on vibes, but to make authority expansion evidence-based.
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Escalation is not an error path for AI agents
As agent systems move from demos into real workflows, escalation should stop being treated as a failure fallback. It is one of the main design surfaces for making AI work usable, governable, and trusted.
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Agent frameworks are becoming control decisions, not library decisions
As Google, AWS, and the broader AI market push agent-building tools into the enterprise, the important choice is no longer only which framework feels easiest. It is which control model a team is committing to.
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The next agent design job is drawing better stop lines
Enterprise AI adoption is rising, but trust is not keeping pace. The more practical problem is not only building capable agents. It is deciding where they must pause, escalate, or hand work back before scope, risk, and cleanup start compounding.
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The next agent management problem is context trust
As shared workspace agents spread across ChatGPT, Slack, browsers, and internal tools, the practical risk is no longer only what agents can access. It is also what they should trust when outside content can quietly steer long-running work.
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Agents are becoming identities, not just tools
Recent moves from Cloudflare and OpenAI point to a deeper infrastructure shift: serious AI systems are no longer only adding tools to prompts. They are starting to treat agents as distinct actors with identity, access, and policy boundaries inside real operating environments.
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The next agent security problem is not only compromise
A more serious agent-security conversation is starting to emerge: the dangerous case is not only a hacked or jailbroken system, but a well-functioning agent that is allowed to act and still acts unwisely inside its permissions.
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How to tell whether an AI workflow should be a workflow, an agent, or a hybrid
A practical way to choose between deterministic automation, agentic execution, and a mixed design — based on risk, ambiguity, exception load, and how much judgment the work actually needs.
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If your agent keeps sending reminders, you built a reminder, not a worker
A simple design test for autonomous AI workflows: if the system keeps nudging a human instead of progressing the task, the job was designed as a reminder loop, not an execution workflow.