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# AI Agents — Overview and Context

Short Description of Research Question

What are AI agents (agentic AI), how are they defined and classified, what practical frameworks and tools exist for building them, what real-world examples and industry perspectives exist, and what are the main benefits, risks, and benchmark/evaluation issues?

## Summary of Findings

- Definitions & conceptual foundations
  - The term "intelligent agent" refers to any entity that perceives its environment, takes actions autonomously to achieve goals, and may improve via learning or knowledge acquisition (Wikipedia). "Agentic AI" describes modern systems that proactively pursue goals, plan, integrate tools, and act over extended periods, usually powered by LLMs and orchestration software.

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# Research on Large Language Models (LLMs) for Low Resource Languages

## Short Description of Research Question
How do recent studies address the challenges and improvements of LLMs and other language technologies for low-resource languages?

## Summary of Work

Recent research on LLMs and NLP for low-resource languages addresses diverse challenges including data scarcity, linguistic bias, domain specificity, and evaluation dataset creation. Major themes include:

1. Workshop Overview: The LoResLM 2025 workshop showcased 35 papers focusing on linguistic inclusivity in NLP for low-resource languages across multiple language families and research areas.

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# Reranking with Large Language Models (LLMs)

## Short Description of Research Question

How to efficiently rerank hypotheses or retrieved passages using Large Language Models to optimize for quality metrics beyond model probability, while managing the computational cost?

## Summary of Work

1. **EEL: Efficiently Encoding Lattices for Reranking (2023)**
   - Investigates reranking hypotheses for conditional text generation by encoding lattices of outputs efficiently with Transformers.

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# Context Engineering AI - Automated Research Attempt

Short Description of Research Question

- Research topic: "context engineering" in AI — definitions, practices, tools, notable papers, tutorials, and community resources.

## Summary of Findings

I attempted to perform automated web research using the required browser automation tools, but the browsing/navigation tool failed repeatedly and I could not visit external websites to extract information. Below are details of the attempts and errors encountered. Because I could not visit any pages, I do not have research findings; instead I have a record of the failed attempts and next recommended steps.

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# Context engineering and context rot

Short Description of Research Question

What is "context rot" (the failure modes of LLMs as context length grows) and what context-engineering practices and mitigations are recommended by recent research and industry sources?

## Summary of Findings

- Definition: "Context rot" refers to the phenomenon where increasing the amount of tokens in a model's context window (longer inputs / longer histories) leads to degraded, inconsistent, or unreliable model performance — e.g., forgetting facts in the middle of long documents, hallucinations, or refusals.

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# AI Agent Harness Survey

Short Description of Research Question

I surveyed major open-source projects and leaderboards for evaluating language models (LLMs), multimodal models (LMMs), and agent systems to answer: "What existing harnesses, frameworks, and leaderboards are available for evaluating LLMs/LMMs and building/running agent harnesses?"

## Summary of Findings

- lm-evaluation-harness (EleutherAI)
  - A mature, widely-used framework for few-shot evaluation of language models. Supports many model backends (HF transformers, vLLM, GGUF/llama.cpp, OpenAI/Anthropic/TextSynth APIs, vLLM, SGLang, NeMo, etc.), many tasks (>60 academic benchmarks), flexible prompt templating (Jinja2, Promptsource), caching, logging, and hub integration. Backend for Hugging Face's Open LLM Leaderboard.

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# What Comes After LLMs — Research Notes

Short Description of Research Question

What are the leading ideas, research directions, and likely near-term and longer-term evolutions in AI that could follow, complement, or supplant large language models (LLMs)?

## Summary of Findings

Across the sources reviewed, there is broad agreement that LLMs will remain important but are likely to be complemented (not immediately replaced) by a range of approaches that address LLM weaknesses (hallucination, lack of continuous learning, heavy compute, limited reasoning, limited embodiment). Key themes:

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# Agentic Context Engineering for AI Agents — Concepts, Benchmarks, and Practices

Short research on how AI agents select, manage, and structure information in their limited context windows (“context engineering”), with a focus on evidence from recent benchmarks and framework docs.

## Summary of Findings

- What “context engineering” means: deciding what information an agent puts into its prompt at any moment. Agentic context engineering shifts that decision to the agent itself via retrieval, search, and memory operations instead of humans hand-curating prompts. [Letta – Context-Bench]
- Why it matters: models do not use long context uniformly—performance degrades as inputs get longer, and structure, distractors, and semantic similarity all influence outcomes (“context rot”). This makes targeted, minimal, well-structured context critical. [Chroma Research]
- Finite context and reliability limits: classical agent components (planning, memory, tool use) are constrained by context length, and natural-language I

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# Context Engineering for AI Agents — Key Patterns and Best Practices (2025)

Short Description of Research Question

What is “context engineering” for AI agents and what practical strategies, pitfalls, and best practices are recommended by current leading sources?

## Summary of Findings

Context engineering is the art and science of filling an LLM’s context window with the right information at each step of an agent’s trajectory. Two recent, influential sources converge on a practical toolkit and operating principles:

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# Building MCP Servers

How to build a Model Context Protocol (MCP) server, with steps and example code references.

## Summary of Findings

- Core capabilities an MCP server can expose:
  - Resources: file-like data clients can read (e.g., API responses, file contents)
  - Tools: functions callable by the LLM (with user approval)
  - Prompts: prewritten templates to help users perform tasks