Research Frontiers: AGI and Beyond
Open problems in agent robustness, long-horizon memory, and multi-agent coordination.
The State of AI Agents in 2025
As of 2025, AI agents powered by large language models can reliably complete complex multi-step tasks, use tools, reason across modalities, and operate with limited supervision. Yet several fundamental challenges remain unsolved before agents reach true general capability.
This lesson surveys the open research frontiers that define the next generation of AI.
Open Problem 1: Long-Horizon Memory
Current LLMs have context windows of 128K–1M tokens — impressive but still limited for tasks that span months of work. The open problem: how to reliably compress, retrieve, and reason over truly long-horizon memory without losing critical details or introducing hallucinations.
# Illustration of long-horizon memory challenges:
LONG_HORIZON_CHALLENGES = {
'compression': {
'problem': 'Summarising months of interactions loses nuance',
'current_approach': 'Hierarchical summarisation (recent detail, old summary)',
'limitation': 'Important details get compressed away; hallucination risk in summaries'
},
'retrieval': {
'problem': 'Finding the relevant memory among millions of entries',
'current_approach': 'Embedding-based similarity search (vector databases)',
'limitation': 'Semantic similarity does not always match relevance; false negatives'
},
'reasoning_over_time': {
'problem': 'Connecting observations from 6 months apart',
'current_approach': 'Temporal indexing + LLM reasoning',
'limitation': 'LLMs struggle with precise temporal ordering of distant events'
}
}
for challenge, details in LONG_HORIZON_CHALLENGES.items():
print(f'{challenge}: {details["limitation"][:80]}')All lessons in this course
- From Assistant to Autonomous Agent
- World Models and Predictive Planning
- Alignment Challenges in Autonomous Agents
- Research Frontiers: AGI and Beyond