--- title: "Context management for AI agents | IOWarp" description: "Context management for AI agents in science: selecting, placing, and recording the data, tools, and provenance an agent needs, and how IOWarp does it." url: https://iowarp.ai/topics/context-management/ --- > Context management for AI agents in science: selecting, placing, and recording the data, tools, and provenance an agent needs, and how IOWarp does it. # Context management for AI agents. Context management is the work of deciding what an AI agent sees, when, and with what authority: which data, which tool results, which prior decisions, and which limits. For scientific agents it is a systems problem as much as a prompting problem, because the relevant context lives in files, catalogs, schedulers, and instruments, not in a chat window. 01 Why a larger context window is not enough 02 Context as a storage hierarchy 03 Bounded and inspectable 04 How IOWarp approaches it 05 Common questions 01 ## Why a larger context window is not enough A model can only reason over what it is given. Handing it everything is slow, expensive, and hides the evidence that matters. Handing it too little makes it improvise. The useful unit is the smallest trustworthy slice of the scientific world needed for the next step. That slice has several parts with different lifetimes: working context for the current turn, handoffs between agents or sessions, durable project memory, and provenance that must outlive all of them. 02 ## Context as a storage hierarchy Storage systems already solve a similar problem: keep hot data close to compute, move cold data to cheaper tiers, and record where everything is. IOWarp applies the same discipline to agent context. Placement, eviction, and prefetching are as relevant to an agent’s memory as they are to a simulation’s checkpoint files. The same idea reaches into the model itself. KV-cache research in the project treats the inference cache as a tier to be placed and evicted, not an unbounded buffer. 03 ## Bounded and inspectable In science, a result has to be checkable. Context management therefore includes recording what an agent was given and what it was allowed to do, so a reviewer can reconstruct the path from data to claim. How IOWarp approaches it ## Where to go deeper. 01**CLIO Core Python API** Bundle, query, and retrieve context, or work directly with tags, blobs, and search. Open ↗ 02**CLIO Agent** ARC memory, which uses CLIO Core as its default store, carries useful context across sessions, and packs with experts and workers give each one only what its task needs. Continue → 03**CLIO Coder** Guidance for repository context, session handoffs, durable memory, and reviewable run receipts. Open ↗ 04**Anthropic on effective context engineering** Further reading: Curating finite context, retrieving information when needed, and keeping useful state across long tasks. Open ↗ 05**OpenAI on managing session context** Further reading: Trimming and summarizing session history, with the tradeoffs in fidelity, latency, and observability. Open ↗ 06**OpenAI on durable memory and state** Further reading: Capturing durable notes, consolidating them, and resolving precedence between memory and current intent. Open ↗ 07**Google DeepMind’s retrieval-augmented language-model research** Further reading: A research example of language models retrieving external text. Retrieval is one part of context management. Open ↗ 08**Google DeepMind’s GopherCite research** Further reading: Supporting answers with retrieved quotations and abstaining when evidence is insufficient. A citation still needs checking. Open ↗ Applications that use it - A2 ### [Agents on the National Data Platform](https://iowarp.ai/applications/national-data-platform/) MCP tools that let an agent search, stage, and analyze datasets federated through the National Data Platform, first shown on EarthScope data. Demonstrated on EarthScope data - A6 ### [GPU direct I/O and compression](https://iowarp.ai/applications/gpu-data-movement/) CUDA kernels that read and write IOWarp from the GPU, GPU payloads offloaded to host memory and NVMe, and compression on the device. A research thread built on NVIDIA technologies. Research thread across the stack Common questions ## Common questions. **Is context management the same as retrieval-augmented generation?** Retrieval is one mechanism. Context management also covers placement across tiers, lifetimes, authority, and provenance, so the agent’s inputs can be bounded and audited. **Where does IOWarp implement it?** CLIO Core provides the runtime and engines; CLIO Agent and CLIO Coder apply it to agent memory and engineering work. Related ## Research and demos on this topic. - [Research direction **Context with a lifecycle** What should survive a turn, a handoff, or a project?](https://iowarp.ai/research/directions/#context-lifecycle) - [Research direction **Memory as a storage hierarchy** Which facts belong hot, durable, or replayable?](https://iowarp.ai/research/directions/#memory-hierarchy) - [Demo **EarthScope through the National Data Platform, 2026** Planned for 2026](https://iowarp.ai/research/demos/#demo-earthscope-through-the-national-data-platform-2026) - [Demo **GPU payload offload** Planned for 2026](https://iowarp.ai/research/demos/#demo-gpu-payload-offload) - [Demo **EarthScope seismograph analysis** Recorded 2025](https://iowarp.ai/research/demos/#demo-earthscope-seismograph-analysis) ## References 1. [Islam, Cernuda, Muradli, Kougkas, Sun. Towards an AI-driven scientific workflow. IEEE eScience 2025 (poster)](https://doi.org/10.1109/eScience65000.2025.00074) 2. [Chitty-Venkata, Ye, Raskar, Kougkas, Sun, Emani, Vishwanath, Nicolae. PagedEviction: Structured block-wise KV cache pruning for efficient LLM inference. Findings of EACL 2026](https://doi.org/10.18653/v1/2026.findings-eacl.168) 3. [Ye, Maurya, Chitty-Venkata, Nicolae, Kougkas, Sun. PKAS: Predictive KVCache-aware scheduling for faster LLM and transformer inferences. HPDC 2026](https://doi.org/10.1145/3806645.3807819) 4. [Logan et al., MegaMmap: Blurring the boundary between memory and storage for data-intensive workloads, SC 2024](https://doi.org/10.1109/SC41406.2024.00114) 5. [Anthropic on effective context engineering](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) 6. [OpenAI on managing session context](https://developers.openai.com/cookbook/examples/agents_sdk/session_memory) 7. [OpenAI on durable memory and state](https://developers.openai.com/cookbook/examples/agents_sdk/context_personalization) 8. [Google DeepMind’s retrieval-augmented language-model research](https://deepmind.google/blog/improving-language-models-by-retrieving-from-trillions-of-tokens/) 9. [Google DeepMind’s GopherCite research](https://deepmind.google/blog/gophercite-teaching-language-models-to-support-answers-with-verified-quotes/) ## Other concepts - [T2 Scientific AI agents](https://iowarp.ai/topics/scientific-agents/) - [T3 MCP servers for scientific computing](https://iowarp.ai/topics/mcp-for-science/) - [T4 HPC I/O and storage hierarchies](https://iowarp.ai/topics/hpc-io/) - [T5 Data movement for AI-infused workflows](https://iowarp.ai/topics/data-movement/) - [T6 Scientific workflows and reproducible deployment](https://iowarp.ai/topics/scientific-workflows/) - [T7 Evidence-based autonomous research](https://iowarp.ai/topics/evidence-based-research/) Next step ## See this topic at work. [Agents on the National Data Platform](https://iowarp.ai/applications/national-data-platform/) [All concepts](https://iowarp.ai/docs/#concepts)