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| - [[state of ai agents]] in [[2024]] |
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| tags:: year | ||
| alias:: 2024 | ||
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| alias:: citizen | ||
| alias:: citizen, ai agents, agents | ||
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| - what is [[avatar]]? |
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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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| - [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 |