Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Comprehensive survey examining how agentic AI systems adapt through post-training, memory architectures, and skill acquisition for long-horizon task execution.
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
As agentic systems move into production, a critical challenge emerges: how do agents adapt to new tasks and improve over time without constant retraining? A new survey examines three key mechanisms—post-training, memory architectures, and skill composition—that enable agentic adaptation at scale.
What changed. Leading systems now use memory-augmented planning where agents retrieve relevant past trajectories and use them as in-context examples for new tasks. This approach (exemplified by JARVIS-1) enables rapid task adaptation without full model retraining.
Why it matters. Long-horizon task execution requires agents to learn from experience. Memory architectures that surface relevant past trajectories dramatically improve planning quality and reduce failure rates on novel tasks. This is especially critical for enterprise deployments where retraining is expensive.
Builder takeaway. Prioritize trajectory-based memory systems in your agent stack—they’re practical, empirically effective, and don’t require model updates. Focus on retrieval quality and relevance ranking to maximize in-context learning gains.