Frequently Asked Questions About Walsenuk Stop Babysitting Agents Framework
22 answers covering everything from basics to advanced usage.
// Basics
How do I diagnose where my team is on the Context Ladder?
Ask three questions: (1) Do your agents run without a human triggering each job? If no, you're at stage (b). (2) Do you maintain static context files that agents read? If yes but nothing more, you're at stage (c). (3) Does a machine layer perform runtime retrieval across multiple systems, resolve conflicts, and enforce permissions? If yes, you're at stage (d). Most teams are honest at (b) or (c) and overestimate their maturity.
What systems of record should a Context Engine ingest?
Every place where useful engineering context lives: GitHub (PRs, commit history, code patterns, issues), Slack or Teams (decisions, CTO overrides, tribal knowledge), Jira or Linear (tickets, priorities, sprint context), internal documentation wikis, runbooks, incident postmortems, and SaaS integration platforms like Zendesk. The key insight is that critical decisions often live in Slack threads, not in docs — so your static repo is never complete.
What is a research packet in the Context Engine?
A research packet is the token-optimised, conflict-resolved, permission-scoped output the Context Engine delivers to an agent before execution. It contains exactly the org-specific facts the agent needs to plan and act correctly — naming relevant patterns, existing utilities, correct entry points, and resolved conflicts with citations. It is deliberately small and high-signal. The engine reasons across all retrieved surfaces, strips redundancy, and compresses the result rather than dumping raw data.
What size team needs a Context Engine vs. just using static context files?
Any team that finds agents producing output that gets rejected at PR review. Team size mainly affects urgency around data governance — at 20+ engineers, permission scoping becomes non-negotiable. But even a 5-person team benefits from exhaustive retrieval and conflict resolution if their agents keep missing existing patterns. Static context files (CLAUDE.md) are a reasonable starting point for very small teams but they do not scale and they miss runtime signals like Slack decisions.
What is the doom loop with AI coding agents?
The doom loop is the babysitting cycle where an engineer repeatedly corrects an agent's output — pointing at files, explaining org patterns, re-running prompts, and manually supplying context — because the agent lacks the org-specific knowledge to get it right autonomously. You trigger a job, the agent produces wrong output, you correct it, re-run, it fails differently, and you correct again. The Context Engine breaks this cycle by externalising your role as context supplier into a machine.
// How To
How do I build a social graph for my engineering org?
Point an automated tool at your code repositories. Generate a graph where nodes are engineers and edges represent collaboration signals: who reviews whose PRs, who co-authors code in which services, who pairs with whom. Encode shipping volume as node size. Do not maintain this graph by hand — generate it procedurally from existing data. The graph becomes the pivot point for scoping retrieval to the right codebases and history for each specific engineer.
What happens when two sources contradict each other in the Context Engine?
The Context Engine must surface and settle the conflict — never silently pick one source. Apply authority-weighting rules: recency (newer information wins by default), role (CTO directive outweighs a peer's off-hand comment), and canonicity (official documentation outweighs a casual Slack message). Surface the conflict and its resolution to the agent with citations. Log all conflicts for human review because they often signal architectural drift or undocumented decisions.
How do I handle data governance and permissions in a Context Engine?
Carry authentication context (OAuth model) through every retrieval call from day one. Private Slack DMs, restricted channels, and confidential data must never surface to a requester who lacks permission, even if the agent's query would benefit from that data. If your org has 20+ engineers, this is non-negotiable. Design the engine to return only what the requesting identity is authorised to see. Do not retrofit permissions after the engine is already ingesting Slack — build it in from the start.
How does the plan-execute-review loop work with a Context Engine?
The Context Engine is used at two critical junctures. First, before execution, it produces an org-aware plan by delivering a research packet with correct patterns, conventions, and resolved conflicts. The agent executes using this plan as its harness. Then at code review, the engine evaluates the output against real patterns, past decisions, and current truth. This three-phase loop produces PRs that get 'nitpick and merge' instead of 'this would break everything.'
How do I handle Slack tribal knowledge in my Context Engine?
Slack is where critical engineering decisions often live — CTO overrides, architectural choices, 'we tried X and it broke Y' signals. Ingest Slack as a first-class system of record alongside GitHub and docs. Apply permission scoping so private channels and DMs are only surfaced to authorised requesters. When Slack content contradicts code or docs, use conflict resolution with authority-weighting (role + recency) to settle the truth. Never ignore Slack data; it often contains the real reason behind a codebase pattern.
// Troubleshooting
Why shouldn't I cache Context Engine responses for better latency?
Caching a correct answer is equivalent to writing documentation — it begins decaying the moment you write it. In active codebases, a cached answer to 'what is our Zendesk pattern?' is probably wrong 24 hours later because code, decisions, or priorities changed. A cached correct answer becomes a confident lie. Instead, optimise latency through better retrieval architecture — parallel fan-out, pre-indexed surfaces, incremental graph updates — not answer caching.
My agents keep reinventing shared utilities that already exist — how do I fix this?
This is a textbook case of Satisfaction of Search. Your agent finds a plausible implementation approach and stops looking before discovering the existing utility. Replace naive retrieval with exhaustive multi-surface retrieval that scans your monorepo's lib and service directories. Use the social graph to identify who authored and maintains shared utilities. Inject a pre-execution research packet that explicitly names reusable components. The agent will use existing code instead of hallucinating new implementations.
What if my team is only at the autocomplete stage — should I jump straight to building a Context Engine?
No. The Context Ladder exists for a reason. If you're at stage (a) — fancy autocomplete with no agentic loops — first establish agentic workflows and experience the doom loop at stage (b). Then build static context files at stage (c) to learn what context matters most. Only then invest in a full Context Engine at stage (d). Skipping stages means you won't understand what the engine needs to solve. Each stage teaches you what's missing.
// Comparisons
What is the difference between a Context Engine and naive RAG for AI agents?
Naive RAG retrieves the first plausible chunks from a single vector store and passes them to the agent — triggering Satisfaction of Search. A Context Engine fans out across all systems of record (GitHub, Slack, Jira, docs) in parallel, runs exhaustively until no new signals remain, resolves conflicts between contradictory sources using authority-weighting, enforces permissions, and compresses results into a token-optimised research packet. RAG is a retrieval technique; a Context Engine is an intelligent infrastructure layer.
Can I use CLAUDE.md or agents.md files instead of building a full Context Engine?
Static files like CLAUDE.md and agents.md represent the Curated Context Layer — stage (c) on the Context Ladder. They are better than nothing but fundamentally limited: they go stale, contain no runtime signals, miss conversational decisions made in Slack, and require manual maintenance. They cannot resolve conflicts, enforce permissions, or personalise retrieval per engineer. Think of them as a stepping stone, not a destination.
How is the Stop Babysitting Agents Framework different from just adding more tools to my agent?
Adding more MCP connections or tools gives your agent access but not understanding. The agent still doesn't know what it doesn't know — like a day-one engineer who has access to the entire codebase but no idea a shared service exists. The framework's core insight is that access is not understanding. You need a Context Engine that constructs structured research queries, performs exhaustive retrieval, resolves conflicts, and delivers curated context — not just more API endpoints.
What's the difference between exhaustive retrieval and regular vector search?
Regular vector search returns the top-k semantically similar chunks and stops — triggering Satisfaction of Search. Exhaustive retrieval constructs a structured research query from the agent's intent, fans out across all systems of record in parallel, and runs until no new relevant signals remain. It then reasons across results before returning anything. The difference is like a junior engineer googling once vs. a senior engineer systematically checking code, PRs, Slack decisions, and docs before answering.
// Advanced
Can I use the Context Engine for things other than coding agents?
Yes — the same engine that serves background coding agents should also serve: Ask Engineering Slack channels (auto-detect questions, score confidence, respond automatically), ticket enrichment for Jira or Linear, incident triage, onboarding new engineers, and ad-hoc questions from any team member. You get compounding leverage from a single well-built engine because the retrieval, conflict resolution, and permission scoping layers benefit every use case.
Why do large context windows fail for AI coding agents?
Agents cannot reason effectively over massive token volumes. Stuffing a million-token context window with raw data causes the agent to lose focus, miss critical patterns, and produce lower-quality plans. It also dramatically inflates cost. A token-optimised research packet — where the Context Engine has already reasoned across all data, resolved conflicts, and compressed results — consistently outperforms a stuffed context window. Smaller, curated packets produce better plans and cheaper agent runs.
How do I token-optimise the Context Engine output?
The engine must reason across all retrieved surfaces — not just concatenate chunks. Strip redundancy where multiple sources say the same thing. Resolve conflicts so only the settled truth appears. Remove tangential information that doesn't serve the agent's specific task. Return citations for traceability but compress narrative. The goal is the minimum high-signal content the agent needs to plan and act correctly. Test by measuring: does adding more context to the packet improve agent output? If not, you've hit the optimum.
Can the Context Engine work with Claude, Cursor, Codex, or any agent framework?
Yes — the Context Engine is infrastructure-layer, not agent-specific. It sits behind whatever agent framework you use (Claude CLI, Cursor, Codex, custom agents) and delivers research packets via API before the agent executes. The agent framework handles code generation and tool use; the Context Engine handles knowing what the org knows. This separation means you can swap agent frameworks without rebuilding your context infrastructure.
How do I measure whether my Context Engine is working?
Track three metrics: (1) PR acceptance rate — what percentage of agent-generated PRs get merged with only nitpick-level feedback vs. rejected outright; (2) doom loop frequency — how often engineers manually correct and re-run agent tasks; (3) token cost per successful task — a well-built engine should reduce total tokens spent because the agent plans correctly the first time. Secondary signals include time-to-merge and the number of review cycles per PR.