Control-Plane Placement and Memory Substrates in Modern Agentic AI: A Comparative Method for Prompt-Native Systems, Agent Shells, and Durable Runtimes
By: A. Bondarenko, N. Zamorskaia, D. Djakons, A. Djakona and R. Djakons
References
- Liu, J. (2026, March 3). LlamaIndex is more than a RAG framework: It is agentic document processing. LlamaIndex. https://www.llamaindex.ai/blog/llamaindex-is-more-than-a-rag-framework.
- Pan, L., Zou, L., Guo, S., Ni, J., & Zheng, H.-T. (2026). Natural-language agent harnesses [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.25723
- Zhou, C., Chai, H., Chen, W., Guo, Z., Shan, R., Song, Y., . . . & Zhang, W. (2026). Externalization in LLM agents: A unified review of memory, skills, protocols and harness engineering [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.08224
- Yue, L., Bhandari, K. R., Ko, C.-Y., Patel, D., Lin, S., Zhou, N., … & Pan, S. (2026). From static templates to dynamic runtime graphs: A survey of workflow optimization for LLM agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.22386
- Rombaut, B. (2026). Inside the scaffold: A source-code taxonomy of coding agent architectures. [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.03515
- Anthropic. (2026, January 9). Demystifying evals for AI agents. https://www.anthropic.com/engineering/demystifying-evalsfor-ai-agents.
- Anthropic. (2025–2026). Claude Code materials, including How Claude Code works, Create custom subagents, Agent SDK overview, and Modifying system prompts. https://code.claude.com/docs/en/how-claude-code-works (documentation set).
- GitHub / Microsoft / VS Code team. (2025–2026). Copilot agent-mode and coding-agent materials, including Introducing GitHub Copilot agent mode (preview), Local agents in Visual Studio Code, and About GitHub Copilot cloud agent. https://code.visualstudio.com/blogs/2025/02/24/introducing-copilot-agent-mode (documentation set).
- OpenAI. (2025–2026). Agents SDK and workflow tooling materials, including the Building agents track, Agents SDK guide, Agent Builder guide, and Results and state guide. http://developers.openai.com/tracks/building-agents (documentation set).
- Google. (2025–2026). Agent Development Kit (ADK) materials, including workflow agents, callbacks, and skills documentation. https://google.github.io/adk-docs/agents/workflow-agents/ (documentation set).
- Cline. (2025–2026). Documentation and repository materials, including Plan & Act docs and prompt architecture. https://docs.cline.bot/ (see also the project repository at https://github.com/cline/cline/)
- Anomalyco. (2026). OpenCode repository and session-runtime sources. https://github.com/anomalyco/opencode
- QwenLM. (2025–2026). Qwen Code materials, including Approval Mode, Subagents, Settings, and repository docs. https://qwenlm.github.io/qwen-code-docs/en/users/features/approval-mode/ (documentation set).
- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2201.11903
- Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing reasoning and acting in language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2210.03629
- Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2305.10601
- Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., … & Hoefler, T. (2023). Graph of thoughts: Solving elaborate problems with large language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2308.09687
- Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., … & Clark, P. (2023). Self-refine: Iterative refinement with self-feedback [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2303.17651
- Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2303.11366
- Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023). Chain-of-verification reduces hallucination in large language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2309.11495
- Fricke, F., Malberg, S., & Groh, G. (2026). Framework of thoughts: A foundation framework for dynamic and optimized reasoning based on chains, trees, and graphs. In Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026) (pp. 132–151). Association for Computational Linguistics. https://doi.org/10.18653/v1/2026.surgellm-1.8
- Plaat, A., Wong, A., Verberne, S., Broekens, J., van Stein, N., & Bäck, T. (2025). Multi-step reasoning with large language models: A survey. ACM Computing Surveys, 58(6), 1–35. https://doi.org/10.1145/3774896
- Bondarenko, A., Zamorskaia, N., Djakons, D., Djakona, A., & Djakons, R. (2026). Rapid evolution of agentic AI: From framework labels to control surfaces [Short overview]. Riga Nordic University.
- SWE-bench project. (2024–2026). Official benchmark materials, including the SWE-bench leaderboard and mini-SWE-agent. https://www.swebench.com/ (benchmark site and mini-SWE-agent materials).
- Merrill, M. A., Shaw, A. G., Carlini, N., Li, B., Raj, H., Bercovich, I., . . . & Schmidt, L. (2026). Terminal-Bench: Benchmarking agents on hard, realistic tasks in command line interfaces [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.11868
- Dao, M.-D., Le, Q. M., Lam, H. T., Le, D.-T., Pham, Q.-V., O’Sullivan, B., & Nguyen, H. D. (2026). Agentic design patterns: A system-theoretic framework [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.19752
- Milosevic, Z., & Rabhi, F. (2026). Architecting agentic communities using design patterns [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.03624
- Xu, W., Wang, Y., Mei, K., Liang, K., Wang, Z., Jin, M., … & Metaxas, D. N. (2026). MemGym: A long-horizon memory environment for LLM agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.20833
- Wu, D., Wang, H., Yu, W., Zhang, Y., Chang, K.-W., & Yu, D. (2024). LongMemEval: Benchmarking chat assistants on long-term interactive memory [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2410.10813
- Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024). Evaluating very long-term conversational memory of LLM agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2402.17753
- He, Z., Wang, Y., Zhi, C., Hu, Y., Chen, T.-P., Yin, L., … & Pentland, A. (2026). MemoryArena: Benchmarking agentmemory in interdependent multi-session agentic tasks [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2602.16313
- Shen, Y., Li, K., Zhou, W., & Hu, S. (2026). Mem2ActBench: A benchmark for evaluating long-term memory utilization in task-oriented autonomous agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.19935
- Li, X., Liu, Y., Chen, W., You, B., Di, Z., He, Y., . . . & Song, D. (2026). SkillsBench: Benchmarking how well agent skills work across diverse tasks [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2602.12670
- Zhu, J., Wu, J., Hu, M., Zhu, S., Pan, J., Shen, W., … & Xu, M. (2026). SWE Context Bench: A benchmark for context learning in coding [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2602.08316
- pi.dev/Zechner, M. (2025–2026). Pi coding agent materials, including the official site and architectural write-up on explicit artifact memory. https://pi.dev/ (see also https://mariozechner.at/posts/2025-11-30-pi-coding-agent/)
- Nous Research. (2026). Hermes Agent official materials. https://hermes-agent.nousresearch.com/
- Honcho. (2026). Honcho design patterns and memory-service architecture materials. https://honcho.dev/docs/v3/documentation/core-concepts/design-patterns
- Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023). Cognitive architectures for language agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2309.02427
- Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023). MemGPT: Towards LLMs as operating systems [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2310.08560
- Du, P. (2026). Memory for Autonomous LLM Agents: Mechanisms, evaluation, and emerging frontiers. [Preprint]. arXiv. https://arxiv.org/abs/2603.07670
- Nous Research. (2026). Hermes Agent memory documentation. https://hermes-agent.nousresearch.com/docs/user-guide/features/memory
- Fang, J., Xu, B., Wang, Z., Cao, H., Deng, X., Dong, B., … & Zhang, N. (2026). Rethinking memory as continuously evolving connectivity [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.28773
- Chen, H., Zhang, Z., Pei, W., He, B., Wu, M., Zeng, J., … & Zhang, H. (2026). MemForest: An efficient agent memory system with hierarchical temporal indexing [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.23986
- Latimer, C., Boschi, N., Neeser, A., Bartholomew, C., Srivastava, G., Wang, X., & Ramakrishnan, N. (2025). Hindsight is 20/20: Building agent memory that retains, recalls, and reflects [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2512.12818
- Yu, Y., Yao, L., Xie, Y., Tan, Q., Feng, J., Li, Y., & Wu, L. (2026). Agentic memory: Learning unified long-term and short-term memory management for large language model agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.01885
- OpenClaw project. (2026). OpenClaw repository and platform materials. https://github.com/openclaw/openclaw
- obra. (2025–2026). superpowers repository. https://github.com/obra/superpowers
- glebis. (2025–2026). claude-skills repository. https://github.com/glebis/claude-skills
- Anthropic. (2025–2026). skills repository and marketplace materials. https://github.com/anthropics/skills
- GitHub. (2025–2026). awesome-copilot repository. https://github.com/github/awesome-copilot
- LangChain. (2025–2026). LangGraph materials, including the LangGraph overview, persistence docs, and workflowsand-agents documentation. https://docs.langchain.com/oss/python/langgraph/overview (documentation set).
- Deus Data. (2026). codebase-memory-mcp: High-performance code intelligence MCP server with persistent knowledge graph. https://github.com/DeusData/codebase-memory-mcp (see also the preprint at https://arxiv.org/abs/2603.27277).
- Shamsi, S. (2026). graphify: AI coding assistant skill for building queryable knowledge graphs from any codebase. [Computer software]. GitHub. https://github.com/safishamsi/graphify.
- CocoIndex. (2026). cocoindex-code: Lightweight embedded AST-based code search engine and MCP server for coding agents. [Computer software]. GitHub. https://github.com/cocoindex-io/cocoindex-code.
- Li, X., Ben-Israel, N., Raz, Y., Ahmed, B., Serebro, D., & Raux, A. (2026). RepoMod-Bench: A benchmark for code repository modernization via implementation-agnostic testing [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2602.22518
- Heule, S., Jia, E., & Jain, N. (2025, November 6). Improving agent with semantic search. Cursor. https://cursor.com/blog/semsearch
- Agarwal, A., & Peet, C. (2025, November 19). How we’re making GitHub Copilot smarter with fewer tools. The GitHub Blog. https://github.blog/ai-and-ml/githubcopilot/how-were-making-github-copilot-smarter-with-fewer-tools/
- Anthropic. (2026, February 5). Quantifying infrastructure noise in agentic coding evals. https://www.anthropic.com/engineering/infrastructure-noise
- Wu, D., Ji, Z., Kawatkar, A., Kwan, B., Gu, J.-C., Peng, N., & Chang, K.-W. (2026). LongMemEval-V2: Evaluating long-term agent memory toward experienced colleagues [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.12493
- Wang, H., Mang, Q., Cheung, A., Sen, K., & Song, D. (2026, April 8). How we broke top AI agent benchmarks: And what comes next. Berkeley RDI. https://rdi.berkeley.edu/blog/trustworthy-benchmarks-cont/
- Anthropic. (2024, November 25). Introducing the Model Context Protocol. https://www.anthropic.com/news/model-context-protocol
- Zietsman, C. (2026). The specification as quality gate: Three hypotheses on AI-assisted code review [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.25773
- Żywot, A., Chen, X., Yuan, Y., Søgaard, A., & de Rijke, M. (2026). Can small agents collaborate to beat a single large language model? [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.11327
- Kulkarni, S., & Kulkarni, Y. (2026). Benchmarking multi-agent LLM architectures for financial document processing: A comparative study of orchestration patterns, cost-accuracy tradeoffs and production scaling strategies [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.22651
DOI: https://doi.org/10.2478/lpts-2026-0036 | Journal eISSN: 2255-8896 (formerly 0868-8257) | Journal ISSN: 0868-8257
Language: English
Page range: 46 - 64
Published online: Sep 21, 2026
Published in print: Oct 1, 2026
Published by: Institute of Physical Energetics
In partnership with: Paradigm Publishing Services
Keywords:
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© 2026 A. Bondarenko, N. Zamorskaia, D. Djakons, A. Djakona, R. Djakons, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.