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Presentation: Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP

Ajay Prakash discusses how LinkedIn overcomes AI agent limitations in large codebases.

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Why am I seeing this Ranked on source trust — InfoQ

It ranks mainly on source trust: InfoQ is the most reliable outlet we track on this subject, and is the only one on the story so far.

Single-sourced. No second organisation has confirmed it yet.

Link-outLink-out, because only one organisation carries it and no first-party source is on it. Link-out means we point at the publisher and say nothing of our own.

Blended score 0.473 — every figure below is computed, none of it is editorial.
FactorWeightScore ContributionWhere it came from
Corroboration 0.35 0.39 +0.135 29% 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true.
Source trustleads 0.25 0.75 +0.188 40% InfoQ is the highest-trust source on this story. Trust is taken from the best source, not averaged.
Pickup rate 0.20 0.00 +0.000 0% One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate.
Freshness 0.20 0.75 +0.150 32% Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent.

Corroboration counts distinct organisations, once each, and only from tiers 1 and 2. Freshness halves every 10 hours, so this ranking is a snapshot and will differ at the next build.

Read the full article at Infoq →

What happened

Ajay Prakash discusses how LinkedIn overcomes AI agent limitations in large codebases. He explains Contextual Agent Playbooks and Tools - built on Model Context Protocol (MCP) - which serves procedural memory, code search, and runbooks directly to coding agents. Prakash shares architectural details and operational guardrails that deliver a 20% productivity boost with zero loss in reliability.

1independent orgs
47story score
0velocity
75source trust
2passes seen

How this story arrived

Ordered by when each source was first observed, which is what the velocity figure is computed from. Publishers backdate; observed order does not.

  1. 01 Infoq first seen Presentation: Context Engineering at LinkedIn: How We Built an Organizational Context Layer

Overclock clusters coverage from independent sources and grades it automatically. The figures above are computed, not editorial. This page summarises and links to reporting by the outlets named — follow the links for the original work.