The Three Things Wrong with AI Agents in 2026 (and how we fixed each one)
📰 Dev.to AI
The article discusses three structural issues with AI agents, including siloed memory, and how they can be addressed to improve success rates
Action Steps
- Identify siloed memory issues in AI agent architectures
- Address the problem of siloed memory by implementing shared knowledge graphs or other solutions
- Analyze other structural reasons for agent project failures, such as those mentioned in the article
- Implement fixes for the identified issues to improve agent performance and success rates
Who Needs to Know This
AI engineers and developers working on agentic AI projects can benefit from this article to identify and fix common pitfalls, and improve the overall performance of their agents
Key Insight
💡 Structural issues, such as siloed memory, are a major cause of AI agent project failures, and addressing these issues can improve the success rates of agentic AI projects
Share This
💡 40% of agentic AI projects will be cancelled by 2027 due to structural issues, not model quality. Fixing siloed memory and other problems can improve success rates
DeepCamp AI