Loop Engineering

COG Loop Engineering

> TL;DR: Some COG skills are not one-shot prompts. They are loops: act, observe, verify, decide whether to continue. This skill is the shared vocabulary those skills use. The iron rule: trust deterministic checks, never the agent's own "looks done" self-report. Every loop must declare its verifier, its stopping conditions, and which pattern it follows.

This is a reference and design aid, not a content-generating workflow. Skills that loop (daily-brief, knowledge-consolidation, url-dump, weekly-checkin, and research/triage skills like auto-research and scout) link here instead of restating the rules. Invoke it directly when you are building or fixing an iterative skill.

Why loops

A chain runs fixed steps: A then B then C. A loop is dynamic: the agent takes an action, reads real feedback (a fetched page, a date stamp, a file count), reasons about it, and repeats until a goal is met or a stop condition fires. Most knowledge-work that "keeps going until good enough" is a loop, and COG benefits from naming the loop explicitly rather than hoping a single prompt nails it.

The COG loop

   ┌──────────────────────────────────────────────┐
   │  1. Gather    pull context (vault + sources)   │
   │  2. Act       one step: search / fetch / scan  │
   │  3. Observe   read the real result             │
   │  4. Verify    run the deterministic check      │
   │  5. Update    write progress to a vault file   │
   │  6. Decide    continue?  → loop                │
   │               stop?      → finish + report     │
   └──────────────────────────────────────────────┘

Step 4 is the load-bearing one. A loop without a verifier is just a chain that repeats.

Termination conditions (use layers, never one)

A robust loop needs several exits so it always halts:

| Exit | What it is | Example | |------|-----------|---------| | Deterministic verifier | A mechanical pass/fail that confirms the goal | "Publication date is within 7 days" | | Hard iteration cap | Max passes, no matter what | "Stop after 5 searches per topic" | | Budget guard | Max time / tool calls / tokens | "Stop after 20 fetches total" | | No-progress detection | Recent passes changed nothing | "2 searches in a row found nothing new" | | Human escalation | Hand a stuck loop back to the user | "Asked twice, still unclear: ask the user" |

Pick the verifier plus at least one safety exit (cap or budget) for every loop. No-progress detection is what stops the quiet infinite loops that a cap alone misses.

Verification first (COG's rule, applied to loops)

COG is verification-first: no hallucinations, sources required. Inside a loop that means:

• Prefer mechanical checks. A date comparison, a source count, a "required field is non-empty", a "file marked consolidated" check cannot be gamed and cannot be hallucinated.
• Reserve judgment-based checks for the genuinely unquantifiable (is this theme actually new? is this summary faithful?). When you must use judgment, state confidence and link evidence.
• Never accept the agent's own "I think this is complete." That is the single most common way loops produce confident garbage.

In-loop context management

Long loops fill the window with old tool output and start to drift ("context rot"). Counter it:

• Externalize state to the vault. Write progress to the output file as you go. The vault file is the memory; the conversation is scratch.
• Compact and prune. Summarize finished passes into a line or two. Drop raw page text once you have extracted what you need.
• Isolate sub-agents. In agent_mode: team, give each worker only the slice it needs and take back only its conclusion, so one subtask runs in a clean window. Never paste one worker's raw output into the next worker's prompt.

Named patterns

| Pattern | Shape | Where COG uses it | |---------|-------|-------------------| | Act-observe (ReAct) | reason → act → observe → repeat | base of every COG loop | | Reflect-retry (Reflexion) | on failure, write the lesson, retry differently | url-dump / scout fetch retries, daily-brief re-search | | Plan-execute-verify | plan steps, run them, verify each | knowledge-consolidation passes | | Evaluator-optimizer | generate, score against criteria, repeat until it passes | daily-brief item verify, url-dump quality gate | | Orchestrator-workers | split into subtasks, run in fresh windows, synthesize | team-mode scans, auto-research threads, team-brief | | Loop-until-dry | keep going until K passes in a row surface nothing new | knowledge-consolidation theme extraction | | Human-in-the-loop | escalate or ask when the loop is stuck or the call is the user's | weekly-checkin reflection, onboarding |

Failure modes and fixes

| Failure | Fix | |---------|-----| | Context overflow / drift | compact, prune, externalize to vault, isolate sub-agents | | Silent infinite loop | no-progress detection plus a hard cap | | Hallucinated success | trust the deterministic verifier, never self-report | | Compounding errors | verify early and every pass, not only at the end | | Cost blowup | budget guard, and stop at "good enough", not "perfect" | | Goal drift | keep the goal and stop conditions written at the top of the loop's state |

How skills use this

A skill's ## Loop Engineering section should be short and concrete. It names:

1. The loop in one or two lines (what repeats).
2. The verifier (the mechanical pass/fail).
3. The termination conditions (verifier plus safety exits).
4. The pattern(s) from the table above.

It does not restate this skill. It points here.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: huytieu
• Source: huytieu/COG-second-brain
• License: MIT

Install and usage instructions live in the source repository linked above.