Mahmudul Hassan

Software Engineer | Node | Laravel | WordPress | Vue | React | DevOps

[Mahmudul Hassan @promahmudul]

AI code needs a delivery system. An agent can produce a patch quickly. Production value comes from tests, review, security checks, deployment rules and monitoring. Measure accepted changes and escaped defects, not generated lines. #AIEngineering

[Mahmudul Hassan @promahmudul]

Agents working for days need checkpoints. Save completed steps, tool results, pending approvals and the next action. After failure, resume from the last safe point without repeating earlier work. A long-running agent is a durable workflow. #AIAgents

[Mahmudul Hassan @promahmudul]

Let AI reason. Keep business rules fixed. Use AI for requests and exceptions, but keep refund limits, payment approvals, access changes and compliance steps in code and approval gates. Ambiguity is for the model. Boundaries are for the system. #AIAgents

[Mahmudul Hassan @promahmudul]

Your AI needs your business definitions. OpenAI’s Data agent uses company metric definitions and existing data permissions. Give every KPI an owner, formula, source and refresh schedule, or AI can scale inconsistent answers. #EnterpriseAI

[Mahmudul Hassan @promahmudul]

Google Cloud and Accenture plan a 1,000-person forward-deployed engineering workforce for Gemini Enterprise. The signal: AI value is won in the last mile. Put engineers beside process owners to map exceptions, data and handoffs. #EnterpriseAI

[Mahmudul Hassan @promahmudul]

Sensitive work does not always need the cloud. NVIDIA’s PAIR beta routes separate AI requests across compatible local computers while keeping prompts, files and agent context on the network. Local for privacy. Cloud for capability and scale. #LocalAI

[Mahmudul Hassan @promahmudul]

Test what an AI agent does when the task becomes impossible. Remove a required file, break an integration or create conflicting instructions. It should stop, explain the blocker and escalate. If it invents a shortcut, it is not ready for production. #AIAgents

[Mahmudul Hassan @promahmudul]

Leah, Oracle and PwC are combining domain AI software, cloud infrastructure and implementation expertise. That is the enterprise AI product: technology plus delivery. Buyers want confidence the system will survive contact with real operations. #EnterpriseAI

[Mahmudul Hassan @promahmudul]

Your AI workflow needs a manual mode. If a model API, cloud service or integration stops, the process should pause safely, queue unfinished work and hand control to a person. Automation should not become a single point of failure. #AIAgents

[Mahmudul Hassan @promahmudul]

Your AI agent needs an incident log. If it can message customers, change records, deploy code or trigger payments, preserve the input, tool call, permission, result and human intervention. Review near misses before they become failures. #AIAgents #AISafety

[Mahmudul Hassan @promahmudul]

Being first to launch an AI agent does not mean reaching ROI first. Salesforce’s new survey points to the stronger preparation: clean data, narrow scope and a clear human escalation path. Prove one bounded workflow, then expand. #AIAgents #Automation

[Mahmudul Hassan @promahmudul]

Workday says over half its Q2 net-new wins included an AI solution. The signal for SaaS founders: AI is becoming part of the buying decision. Embed it in the core workflow customers pay for, then prove the outcome. #SaaS #AI

[Mahmudul Hassan @promahmudul]

Tencent’s open Hy4 release is another reminder: the model is not your moat. Durable value comes from business context, workflow logic, integrations, permissions, evaluations and outcomes. Build so the model can change without rebuilding the operation. #AI #SaaS

[Mahmudul Hassan @promahmudul]

AI agents are leaving the screen. Once software can move physical equipment, “undo” may not exist. Define operating limits, hard stops, simulation tests, manual override and action logs before giving an agent control. #AIAgents #Automation

[Mahmudul Hassan @promahmudul]

Automate the work before cutting the team. AI activity is not operational capacity. Measure completed outcomes, errors, rework and human intervention before reducing headcount based on expected automation. #AI #Automation

[Mahmudul Hassan @promahmudul]

Google launched industry-specific Gemini Enterprise offerings for legal and finance. The signal: buyers want AI tied to real workflows, trusted data and domain rules. Start with one costly process, not a general chatbot. #EnterpriseAI #Automation

[Mahmudul Hassan @promahmudul]

AI coding shifted the bottleneck to product decisions. Agents can implement quickly, but they still need clear users, behaviours, edge cases and acceptance criteria. If the requirement is vague, AI builds the wrong thing faster. #AIEngineering #Product

[Mahmudul Hassan @promahmudul]

Coding agents need a budget, not just access. Google’s Antigravity rollout adds pooled quotas, spend controls and usage metrics. Once agents become shared infrastructure, track cost per finished outcome, not tokens alone. #AIEngineering #FinOps

[Mahmudul Hassan @promahmudul]

AI needs the rules your best operators never wrote down. Alteryx says 53% of organizations struggle to translate business context into AI workflows. Before building an agent, document exceptions, thresholds, approvals, and escalation paths. #EnterpriseAI

[Mahmudul Hassan @promahmudul]

AI governance cannot live in a PDF. If an agent can update records, issue refunds, or trigger workflows, its identity, permissions, approval limits, audit trail, and rollback must exist inside the execution path. Written policy cannot stop a bad tool call. #AIGovernance