Your agent works for ten turns, then forgets the goal, leaks a secret, or bills $20K a month. This is the layer that stops all three.
Your agent handled the demo fine. Then it ran fifty steps, filled its window with tool output, forgot the original goal, and quietly obeyed an instruction hidden inside a web page it fetched. Meanwhile the bill tripled. These are context problems, and they only surface once a system is real — multi-turn, multi-agent, tool-heavy, and exposed to text you didn't write. Across ten modules you build the layer that handles them: orchestrators that hand work to workers without leaking everything, assembly that retrieves just in time instead of stuffing the window, hierarchical summaries that survive a hundred rounds without drifting, prompt layouts designed so the key-value (KV) cache actually hits, long-horizon memory built from vector and graph stores with consolidation and reflection, and a red-team suite that proves indirect prompt injection fails against you. Everything is Python, runnable against the real Anthropic and OpenAI model endpoints. You finish with a context platform two features share, plus the cost, latency, and reliability numbers to defend it.
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 applyFinished the tasks? Take the prompt to your AI and get tested on it. We copy the prompt and open the app — just paste it in.
Most turns need three chunks, not thirty. Route, budget, and fetch just in time — and prove the routing actually helped.
Summarize a summary ten times and the facts quietly mutate. Build multi-resolution memory that still remembers turn three.
When an answer is wrong, free-text context gives you nothing to trace. Schemas, versions, and provenance make it auditable.
One timestamp at the top of your prompt can invalidate the whole cache. Learn where prefixes break and what it costs you.
Every tool call adds output the agent never drops. Compact mid-loop, externalize state, and bound the run before it dies.
Retrieval isn't memory. Pick what to store, how to recall it, and what happens when yesterday's fact contradicts today's.
A web page your agent fetched can tell it to email your database. Map the attack surface, then red-team it until it holds.
One screenshot can cost more than a page of text. Decide what to send as an image, what to extract, and where to place it.
Cheap per request, ruinous per million. Model the economics, cache at the infrastructure layer, and degrade before you fall over.
Complete 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.