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Standby MCP cuts agent latency with always-on web servers

A new approach to running AI agents, called Standby MCP, has been developed to reduce latency for real-time interactions. Unlike traditional methods that involve starting a run and polling for completion, Standby MCP keeps a web server continuously active at a fixed URL. This allows agents to send requests and receive responses on the same connection, significantly cutting down the time between a query and its answer, with calls returning in under a second. This method is particularly beneficial for agents that need to ask single, live questions mid-conversation, though it bills per event answered rather than per dataset row delivered. AI

IMPACT Reduces latency for AI agents, enabling more responsive real-time interactions and simpler integration.

RANK_REASON This item describes a new technical approach for running AI agents, focusing on infrastructure and performance improvements for real-time interactions.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Standby MCP cuts agent latency with always-on web servers

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5 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
This item describes a new technical approach for running AI agents, focusing on infrastructure and performance improvements for real-time interactions.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
infra, product
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AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · FetchSmith ·

    Standby MCP: one URL that's always on, instead of an Actor run you poll

    <p>Every other Actor we build follows the same shape: start a run, poll <code>GET /v2/actor-runs/&lt;id&gt;</code><br /> until it's <code>SUCCEEDED</code>, fetch the dataset. That's fine for a batch job — nobody minds a 10-second<br /> startup cost when they asked for 5,000 rows.…