Sources
The key claims in this course rest on the sources below. The lessons cite them with [^Sn]; the exact excerpts sit under each entry.
S1 — Effective context engineering for AI agents — Anthropic Engineering
URL: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
- authority: authoritative-guide
Anthropic engineering's authoritative long-form article on context engineering, and this course's core basis: the relationship between context engineering and prompt engineering, the attention budget and context rot, the "altitude" of a system prompt, tools and examples as context, just-in-time retrieval and progressive disclosure, compaction, structured note-taking, and the subagent architecture — nearly every topic in this course comes from it.
Key quote:
"we view context engineering as the natural progression of prompt engineering" "Prompt engineering refers to methods for writing and organizing LLM instructions for optimal outcomes" "the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference" "An agent running in a loop generates more and more data that could be relevant for the next turn of inference" "LLMs have an "attention budget" that they draw on when parsing large volumes of context" "Every new token introduced depletes this budget by some amount" "as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases" "some models exhibit more gentle degradation than others, this characteristic emerges across all models" "These factors create a performance gradient rather than a hard cliff" "context, therefore, must be treated as a finite resource with diminishing marginal returns" "agents that operate over multiple turns of inference and longer time horizons" "engineers hardcoding complex, brittle logic in their prompts to elicit exact agentic behavior" "vague, high-level guidance that fails to give the LLM concrete signals for desired outputs" "specific enough to guide behavior effectively, yet flexible enough to provide the model with strong heuristics" "tools should be self-contained, robust to error, and extremely clear with respect to their intended use" "building tools that are well understood by LLMs and have minimal overlap in functionality" "returning information that is token efficient and by encouraging efficient agent behaviors" "stuff a laundry list of edge cases into a prompt" "curate a set of diverse, canonical examples that effectively portray the expected behavior of the agent" "maintain lightweight identifiers (file paths, stored queries, web links, etc.)" "the metadata of these references provides a mechanism to efficiently refine behavior" "allows agents to incrementally discover relevant context through exploration" "retrieving some data up front for speed, and pursuing further autonomous exploration at its discretion" "CLAUDE.md files are naively dropped into context up front, while primitives like glob and grep" "taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window" "passing the message history to the model to summarize and compress the most critical details" "preserves architectural decisions, unresolved bugs, and implementation details while discarding redundant tool outputs" "the agent regularly writes notes persisted to memory outside of the context window" "Like Claude Code creating a to-do list, or your custom agent maintaining a NOTES.md file" "Claude playing Pokémon demonstrates how memory transforms agent capabilities in non-coding domains" "specialized sub-agents can handle focused tasks with clean context windows" "returns only a condensed, distilled summary of its work (often 1,000-2,000 tokens)" "the detailed search context remains isolated within sub-agents" "compaction, structured note-taking, and multi-agent architectures" "maintain coherence, context, and goal-directed behavior over sequences of actions"
S2 — Building Effective AI Agents — Anthropic Engineering
URL: https://www.anthropic.com/engineering/building-effective-agents
- authority: authoritative-guide
Anthropic's authoritative article on agent patterns. This course uses it for the loop definition of an agent, the costs and compounding errors of autonomy, and the proportion rule — add complexity only when it demonstrably improves outcomes.
Key quote:
"They are typically just LLMs using tools based on environmental feedback in a loop." "The autonomous nature of agents means higher costs, and the potential for compounding errors." "The LLM will potentially operate for many turns, and you must have some level of trust in its decision-making." "you should consider adding complexity only when it demonstrably improves outcomes."
S3 — How we built our multi-agent research system — Anthropic Engineering
URL: https://www.anthropic.com/engineering/multi-agent-research-system
- authority: authoritative-guide
Anthropic's engineering retrospective on its multi-agent research system, providing first-hand data on subagent context isolation and token economics: parallel subagents each hold an independent context window, subagents act as "intelligent filters" compressing findings for the lead agent, and the token multipliers for agents and multi-agent systems.
Key quote:
"Subagents facilitate compression by operating in parallel with their own context windows" "distributes work across agents with separate context windows to add more capacity for parallel reasoning" "agents typically use about 4× more tokens than chat interactions" "multi-agent systems use about 15× more tokens than chats" "Multi-agent systems work mainly because they help spend enough tokens to solve the problem." "token usage by itself explains 80% of the variance, with the number of tool calls and the model choice" "condensing the most important tokens for the lead research agent" "The essence of search is compression: distilling insights from a vast corpus." "the subagents act as intelligent filters" "agents summarize completed work phases and store essential information in external memory" "agents can spawn fresh subagents with clean contexts while maintaining continuity through careful handoffs"
S4 — Best practices for Claude Code — Claude Code Docs
URL: https://code.claude.com/docs/en/best-practices
- authority: official-docs
Claude Code's official best-practices page (anthropic.com/engineering/claude-code-best-practices now 308-redirects here). Shows what context management looks like in a real product: the discipline around CLAUDE.md, /clear and auto-compaction, subagent investigations isolating the main context, and the headline rule that context is the most important resource.
Key quote:
"Claude's context window fills up fast, and performance degrades as it fills." "The context window is the most important resource to manage." "This matters since LLM performance degrades as context fills." "CLAUDE.md is a special file that Claude reads at the start of every conversation." "CLAUDE.md is loaded every session, so only include things that apply broadly." "Claude loads them on demand without bloating every conversation." "Keep it concise. For each line, ask: "Would removing this cause Claude to make mistakes?" If not, cut it." "Bloated CLAUDE.md files cause Claude to ignore your actual instructions!" "If your CLAUDE.md is too long, Claude ignores half of it because important rules get lost in the noise." "reset context between unrelated tasks. Long sessions with irrelevant context can reduce performance." "Run /clear between unrelated tasks to reset the context window entirely" "During long sessions, Claude's context window can fill with irrelevant conversation, file contents, and commands." "Claude Code automatically compacts conversation history when you approach context limits, which preserves important code and decisions while freeing space." "Since context is your fundamental constraint, subagents are one of the most powerful tools available." "Subagents run in separate context windows and report back summaries" "Scope investigations narrowly or use subagents so the exploration doesn't consume your main context." "A clean session with a better prompt almost always outperforms a long session with accumulated corrections."
S5 — The 2026 Agent Engineering Roadmap — GitHub (codejunkie99/agent-roadmap-2026)
URL: https://github.com/codejunkie99/agent-roadmap-2026
- authority: blog
A community open-source roadmap organized around harness engineering (AI-assisted writing). This course takes only its framing claims: context management as one component of the harness, and its one-line definition of context engineering. Note: its percentage thresholds, token figures, benchmark scores, salary numbers, and other specifics are never cited as fact; "Prompt engineering is dead as a standalone skill in 2026" is that roadmap's opinionated assertion — any citation must attribute it as such, and the course body prefers S1's more careful "natural progression" framing.
Key quote:
"Same model, different harness, completely different result." "the harness is the union of:" "context engineering: deciding what tokens are in front of the model at every step of the loop" "Prompt engineering is dead as a standalone skill in 2026."