Can a Computer-Use Agent Safely Operate Your Legacy ERP?
Computer-use agents can click through legacy ERP clients with no API. Let them query and save drafts; keep posting and master data edits behind human approval.
Notes / Enterprise AI
Topic
Putting AI into real business workflows: the context it needs, the permissions it gets, and the process changes around it.
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Computer-use agents can click through legacy ERP clients with no API. Let them query and save drafts; keep posting and master data edits behind human approval.
When natural-language data queries return two different sales figures, the fix is a governed metric dictionary the model must check before it writes SQL.
A recruiting agent speeds up resume screening, but rejected candidates should not be archived unseen. Set up borderline spot checks, appeals, and audit logs.
Factories and construction sites use AI safety inspection to capture on-site hazards. The algorithm captures risk images. A safety officer verifies, grades, and assigns each case. The responsible team fixes it by the deadline, and the officer closes it after an on-site recheck.
When AI visual inspection lets a defect through, saved images and logs decide who is liable. How to split model and inspector duties and tune manual spot checks.
Companies use bid agents to help prepare tender responses. One mistake on a mandatory clause disqualifies the bid. The agent lists disqualification risks and drafts responses. Commercial and technical staff check each item against the tender documents. People approve the price, the original credentials, and the signed submission.
When AI triages B2B inquiries from WhatsApp, email, and web forms, the lead source field needs fixed rules. Five steps keep channel attribution accurate.
Manufacturers can hand first-pass fault diagnosis to AI. The lasting gain is logging veteran technicians' verified fixes in work orders as searchable knowledge.
A procurement agent's supplier shortlist needs reasons buyers can verify and auditors can trace, tied to certificate dates, report numbers, and delivery records.
Plants hand process data to AI for SPC anomaly detection. When parameters drift, the stop-the-line decision needs a clear split between system and people. The system reads data continuously and suggests causes. Shift leads and quality engineers respond by preset tiers, and people own the stop decision and the restart check.
AI agents now read vendor pages, check specs, compare prices, and send inquiries for B2B buyers. Humans still sign off. What a landing page needs for each reader.
Agent evaluation repeats fixed real business tasks, reports pass rate, cost per run, latency and variation, and lists failures. One run cannot justify launch.
Agent safety guardrails: completion check; caps on steps, tokens, time, repeat calls and tool error streaks. Limit exits are incomplete; log why and open items.
Agent tool approval: code boundary checks, rule auto-approval, human review. Use arguments; ignore model reasons. Strict write checks. Log approvals and blocks.
Call counts and active users don't show what an AI agent delivers. Track cycle time, rework rate, tool-switching cost, and agent overlap against team baselines.
An AI bot marking a chat resolved does not clear the ticket. How to split auto-close from human ticket review, as China's GB/T 47746-2026 standard does.
Remove the AI from an AI-enhanced system and it still runs, only slower. Remove it from an AI Native system and nothing is left. One test to tell them apart.
Split one lead list by task when buying AI outbound calling. AI handles bulk reach, screening, and standard Q&A. Humans handle objections, pricing, and closing.
McKinsey's 2025 survey: 88% of organizations use AI regularly, yet most haven't embedded it in workflows. After AI training, redesign one real workflow first.
An AP agent that runs from invoice matching to payment needs match records, call-back checks on bank changes, duplicate checks, limits, batch review, and recon.
AI agents stall in production when company knowledge conflicts or goes stale. Make context reliable, executable, and auditable, one workflow at a time.
GBrain, Garry Tan's open-source agent memory system, keeps Markdown in Git as the only source of truth and treats its database as a rebuildable index.
Since Aug 26, Grok Bot is in Cursor Pro, SuperGrok, and Cursor Teams. Unlike ChatGPT, it takes a task and keeps working on a cloud computer after you log off.
Hand X brand monitoring to a Bot. Each digest item gets a link, why it matters, and whether a person must decide. Write escalation rules before it starts.
A delivery roadmap that takes an AI idea to a working product, with acceptance criteria at every step. Baize AI designs the path; your team builds the product.
Agents can check IT change requests against configs, runbooks, and schedules. Release approval, change windows, and rollback owners still need human sign-off.
Fine-tuning a vertical model on weekly-changing facts bakes in stale data. Connect an enterprise knowledge base (RAG) first, then fine-tune for tone and format.
A voice sales AI agent cannot take back what it says on a live call. Six handoff triggers, four facts to pass to the rep, and how to audit call recordings.
What is Grok Bot? Learn how xAI's persistent AI agent uses a cloud computer, real Grok Bot use cases, Skills, Routines, Group Chat, and security approvals.
Free. Remote, over video.