🔥 Top Story
APIs aren’t dead. Here’s where MCP fits alongside them.
This PagerDuty-sponsored contributed piece, written by PagerDuty senior product marketing manager Hannah Culver, compares APIs and MCP (Model Context Protocol) specifically through the lens of incident management. It argues MCP doesn't replace APIs but creates a standardized layer through which AI agents can pull context from multiple tools and vendors, addressing the tool sprawl that fragments incident response. APIs retain the edge for deterministic, repeatable workflows — precise endpoint control, and security models teams already understand (API keys, permissions, audit logs, SOC2-relevant compliance) — making them the right choice during time-critical mitigation steps where there's no room for AI interpretation. MCP instead shines in non-deterministic phases like triage, diagnosis, and investigation, where responders need natural-language access to scattered context across alerts, changes, communications, and service ownership. The article gives a concrete example: an agent handling a request like posting a status update about a checkout issue to a comms channel and noting impacted services, drafting the actions and pausing for human approval before executing. A comparison table lays out the pros and cons of each approach, and the piece stresses that MCP's AI-driven tool selection still requires a human-in-the-loop safety layer. The overall framing is that APIs and MCP are complementary rather than competing technologies for incident response teams.
💡 Why it matters: For teams building AI-assisted incident response, drawing the line at deterministic mitigation (API) vs. context-gathering triage (MCP with human approval) is a practical way to adopt agentic tooling without giving up the auditability and control that on-call workflows require.
🔗 Read more · The New Stack
DevOps & Infrastructure
How async processing hides latency and improves responsiveness
The New Stack
This piece is an excerpt from Pekka Engberg's book Latency, distributed as one of three free chapters via ScyllaDB and cross-promoted with P99 CONF, a free virtual latency-focused conference. It explains asynchronous processing as a technique for improving concurrency and hiding latency when further latency reduction isn't feasible. In synchronous processing, tasks run to completion before the next begins — for example, a single-threaded server blocks while waiting for a message to arrive on a socket, then processes it, then sends a response before moving on. Asynchronous processing removes that constraint by using OS-level I/O multiplexing to poll which sockets are readable or writable, letting a server handle multiple independent requests concurrently without blocking a thread; sending a response similarly returns control immediately while the OS transmits it in the background. The excerpt distinguishes asynchronous, concurrent, and parallel processing using a diagram and a sidebar, and walks through an example of a backend needing to talk to two external systems, A and B, to illustrate how an event loop handles overlapping tasks. The overarching point is that deferring noncritical work and performing I/O asynchronously is a practical, proven way to improve perceived responsiveness even when you can't shrink actual latency.
💡 Since async I/O and event loops don't cut raw network latency but instead free up threads to do useful work while waiting, this is the practical lever engineering teams should reach for once physical latency reduction (better hardware, closer regions, faster networks) has hit its limit.
Anthropic’s newest enterprise partner is training 20,000 people on Claude — here’s the shift it signals
The New Stack
Anthropic announced this week that UST — an AI and technology transformation company — has become its second Global Premier Partner in the Claude Partner Network. As part of the deal, UST will embed Claude into the engineering platforms and workflows it builds and runs for enterprise clients, and is training 20,000 of its own technical associates on Claude before rolling those workflows out to customers. UST CEO Krishna Sudheendra said the alliance combines Claude's capabilities with UST's engineering, industry knowledge, and delivery expertise to bring industry-specific platforms to market. A concrete example is UST-iDEC, a hardware and silicon validation platform the company says already cuts validation cycle times by up to 70% and halves typical turnaround; with Claude Code added, the system now reads chip pinouts and hardware schematics directly to auto-write and run regression tests, while Claude's reasoning models compare live edge data against digital twins to catch firmware regressions and signal-integrity faults. Beyond hardware, UST CarePath uses Claude Code and MCP connectors in healthcare to simplify member services and claims processing through an agentic layer with human approval, and UST IntelliOps applies Claude's reasoning to telecom network operations. The article frames this as a broader shift: enterprises standardizing on a single AI stack across teams instead of letting each team pick its own LLM, trading some model-choice freedom for centralized governance and reusable AI workflows.
💡 When a systems integrator standardizes on one AI stack and retrains tens of thousands of staff around it, client organizations that route AI adoption through that partner will likely inherit the same standardized stack — trading per-team model choice for centralized governance, reusable workflows, and auditability.
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