Context engineering for agentic, multi-turn, multi-agent, and adversarial systems at scale: multi-agent context, dynamic assembly, hierarchical memory, KV-cache-aware design, tool-loop management, long-horizon memory, security, multi-modal, and a context platform.
The sequel to Context Engineering. Ten modules, ~100 challenges for systems where context is dynamic, accumulating, shared across agents, adversarial, and operating at scale. Covers multi-agent context architecture, dynamic/JIT assembly, hierarchical & drift-resistant memory, KV-cache-aware design, tool-heavy agent loops, long-horizon memory architectures (vector/graph/episodic + reflection), context security (direct & indirect injection, exfiltration, least privilege, red-teaming), multi-modal context, and building a context platform. Python-first with runnable code against real provider APIs, grounded in 2026 production practice.
Built by Lakshya Kumar
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Design and build the context architecture for a real multi-agent system: per-agent scopes, typed hand-offs, an orchestrator that holds only summaries, long-horizon memory, KV-cache-aware layout, security hardening (isolation + least privilege + red-team suite), and observability. Submit the repo, an architecture doc, the red-team results, and a cost/latency report.
I'm taking "Advanced Context Engineering" — context for agentic, multi-turn, multi-agent, adversarial, and at-scale systems. It covers multi-agent context architecture, dynamic/JIT assembly, hierarchical & drift-resistant memory, KV-cache-aware design, tool-heavy agent loops, long-horizon memory (vector/graph/episodic + reflection), context security (injection/exfiltration/least-privilege/red-teaming), multi-modal context, and building a context platform. Python-first against Anthropic + OpenAI. Here's my context: 1. What I'm building: [multi-agent system / long-running agent / RAG at scale / assistant with memory] 2. My current pain: [context bloat / cost at scale / agent loses the plot / security worries / memory drift] 3. Scale: [requests/day, agents, conversation length] 4. My stack + models: [models, window sizes, vector DB, frameworks] Given that, answer: - Which module should I prioritize and why? - What's the most likely root cause of my current pain, in context-engineering terms? - Name 3 concrete changes I could ship this week and how I'd measure each one's effect on cost, latency, or quality. - Name 1 failure mode I should proactively guard against given what I'm building.
Build an agent that completes a 50+ step task without overflowing its window: mid-loop compaction, externalized state, trajectory pruning, hierarchical memory with checkpoint/resume, and loop bounding. Submit the agent + a chart of bounded context across the run.
Ship a memory system combining vector + graph (or episodic/semantic) stores with write/retrieval policies, consolidation/reflection, and privacy controls, integrated into an assistant. Submit the system + a recall-vs-horizon evaluation and a cross-session learning demo.
Take a RAG+tools agent and harden its context security end-to-end: attack-surface map, isolation, sanitization, least-privilege tools with egress control, and a CI red-team suite. Submit the hardened agent + the red-team report proving known attacks fail and blast radius is limited.
Build a reusable context platform (assembly + caching + compression + observability + security + A/B) that at least two features use, with a cost model, latency optimization, and graceful degradation. Submit the platform, the two integrations, and a cost/latency/reliability report.
Required for the multi-agent and tool-loop modules.