Your Agent Has Amnesia: Why Mobile AI Agents Need Memory to Be Useful
summary
Shows concrete experiments: raw model runs cost under $2 with ~4 model calls but repeatability suffers — 10 runs: 6 successes, 4 failures for Claude; adding structured memory (knowledge graph, episodic and recall stores) cuts retries, recovers from UI drift, and drops some flows from ~$10 to cents. Uses Sonnet, small grounding models (Quen UI, TARS), OCR, OB/SIF CV, a loop‑detector sim check, and a vector DB + accessibility tree audit to enable knowledge graph + vector DB + accessibility tree driven recovery and compaction.
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chapters (18)
- 0:00Mobile AI agents and memory
- 1:05Raw models in production
- 2:33Repeatability in 11 apps
- 3:57Cost curves and harness benefits
- 5:03F1 racing harness analogy
- 6:50Accessibility trees and OCR
- 8:58Knowledge graph for app navigation
- 10:01Memory types: working, episodic, recall
- 11:04Safety: loop detectors and recovery
- 12:47Healing vs replanning in workflows
- 13:31Human escalation and budget
- 14:14Fail fast in CI/CD
- 15:15Memory’s cost and completion role
- 16:19Use cases without accessibility trees
- 17:24Knowledge graph memory
- 19:12Memory efficiency in tasks
- 20:35Key takeaways and frontier
- 21:43Q&A
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