From 4a1df9cd9f18af297ed35c28ffb9d4e9a037fd1d Mon Sep 17 00:00:00 2001
From: master <starodubcev@gmail.com>
Date: Fri, 15 Nov 2024 14:48:07 +0000
Subject: [PATCH] state of ai agents

---
 journals/2024_11_15.md      |  1 +
 pages/54.md                 |  4 ++++
 pages/avatars.md            |  2 +-
 pages/black box problem.md  |  4 ++++
 pages/state of ai agents.md | 25 +++++++++++++++++++++++++
 5 files changed, 35 insertions(+), 1 deletion(-)
 create mode 100644 journals/2024_11_15.md
 create mode 100644 pages/54.md
 create mode 100644 pages/black box problem.md
 create mode 100644 pages/state of ai agents.md

diff --git a/journals/2024_11_15.md b/journals/2024_11_15.md
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+- [[state of ai agents]] in [[2024]]
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diff --git a/pages/54.md b/pages/54.md
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+tags:: year
+alias:: 2024
+
+-
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diff --git a/pages/avatars.md b/pages/avatars.md
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--- a/pages/avatars.md
+++ b/pages/avatars.md
@@ -1,3 +1,3 @@
-alias:: citizen
+alias:: citizen, ai agents, agents
 
 - what is [[avatar]]?
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diff --git a/pages/black box problem.md b/pages/black box problem.md
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+- inability to transparently explain decisions of [[llm]]
+- making it hard to understand how [[llm]] process inputs and generate outputs
+- this creates challenges in debugging, trust, and accountability
+- developers often rely on trial-and-error or additional tools to interpret their behavior
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diff --git a/pages/state of ai agents.md b/pages/state of ai agents.md
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+- [source](http://langchain.com/stateofaiagents)
+- ## adoption and use cases
+	- [[ai agents]] are mainstream: 51% of companies use them, with 78% planning adoption soon
+	- top applications include
+		- summarization: 58%
+		- personal productivity: 54%
+		- customer service: 46%
+	- interest spans tech and non-tech industries alike, showing cross-sector relevance
+- ## key challenges
+	- performance quality is the biggest barrier
+		- especially for small companies
+		- followed by knowledge gaps and time demands
+	- safety concerns and regulatory compliance are significant for enterprises handling sensitive data
+	- understanding and explaining agent behavior remains a [[black box problem]].
+- ## controls and trends
+	- companies rely on tracing, restricted permissions, and offline testing for quality assurance
+	- large firms use more comprehensive guardrails, while startups focus on rapid iteration and monitoring results
+	- multi-agent systems and open-source innovation are driving the next wave of adoption
+- ## actionable takeaways
+	- start small with routine tasks and scale as expertise grows
+	- prioritize performance and safety with tracing, guardrails, and evaluations
+	- leverage open-source tools to accelerate innovation and reduce costs
+	- prepare for future breakthroughs in autonomous multi-agent systems powered by larger ai models
+- ## competitive edge
+	- organizations mastering reliable agents will dominate the shift toward intelligent automation, reshaping workflows with efficiency and precision
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