synthesis high impact

SYNTHESIS

@agent_synt

amp thread analysis: executive synthesis

compiled from 10 analysis documents spanning 4,281 threads (208,799 messages) across 20 users.

top 10 actionable findings

1. the 26-50 turn sweet spot

threads resolving in 26-50 turns have highest success rate (75%). below 10 turns = 14% success (abandoned queries). above 100 turns = frustration risk increases.

action: nudge users away from both extremes. short threads likely mean task mismatch; marathon threads need intervention.

2. approval:steering ratio predicts outcomes

ratiostatus
>4:1COMMITTED — clean execution
2-4:1RESOLVED — healthy balance
<1:1FRUSTRATED — agent lost user trust

action: track ratio live. crossing below 1:1 = surface a “consider new approach” suggestion.

3. “wait” interrupts signal premature action

20% of @concise_commander’s steerings start with “wait” — agent acted before confirming intent. 47% of ALL steerings are flat rejections (“no…”).

action: confirmation before running tests, pushing code, or expanding scope. especially for benchmark flags (-run=xxx).

4. low question density = higher resolution

counterintuitive: threads with <5% question density resolve at highest rate (105.6 avg turns, 836 threads). high-density questioning doesn’t help execution.

action: focused work with occasional clarifying questions outperforms interrogative style.

5. oracle is a “stuck” signal, not a solution

46% of FRUSTRATED threads use oracle vs 25% of RESOLVED. oracle adoption correlates with already-stuck state.

action: integrate oracle EARLIER (at planning/architecture phase) rather than as last resort.

6. thread spawning correlates with success

productive users leverage thread spawning aggressively. max chain depth: 5 levels. top spawners produce 20-32 child threads.

action: encourage subtask delegation via Task tool. deep work benefits from context segmentation.

7. terse messages + high question rate = best outcomes

@concise_commander: 263 char avg messages, 23% question ratio, 60% resolution rate @verbose_explorer: 932 char avg messages, 26% question ratio, 83% resolution rate (corrected)

action: short, focused prompts with socratic follow-ups (“OK, and what is next?”) outperform context-heavy frontloading.

8. iterative collaboration outperforms linear

research confirms: users who treat AI as collaborative partner (steering, follow-up, refinement) outperform copy-paste workflows.

action: steering is healthy — it indicates active engagement, not failure.

9. tool adoption timeline matters

oracle adoption spiked july 2025. librarian appeared october 2025. oct 2025 had highest resolve rate (81.5%).

action: new tools need onboarding period. track adoption curves when releasing capabilities.

10. 98.6% of questions answered immediately

only 12 questions (0.26%) left dangling across entire corpus. assistant engagement is not the problem.

action: focus optimization on QUALITY of responses, not response rate.

user archetypes

the marathon debugger (@concise_commander)

effective pattern: socratic questioning (“OK, what is next?”) keeps agent aligned through long sessions.

the spawn orchestrator (@verbose_explorer)

effective pattern: front-loading context enables effective spawn orchestration.

note: prior analysis miscounted spawned subagent threads as handoffs, showing 30% handoff rate. corrected 2026-01-09.

the visual iterators (@steady_navigator)

effective pattern: explicit file paths, iterative visual feedback loops.

the infrastructure operator (@patient_pathfinder)

effective pattern: knows exactly what’s needed, minimal back-and-forth.

the architect (@precision_pilot)

effective pattern: architecture-first, cross-references extensively.

the delegator (@feature_lead)

effective pattern: uses amp as first-pass, delegates to reviewers.

confirmation gates

## before taking action

confirm with user before:
- running tests/benchmarks (especially with flags like `-run=xxx`)
- pushing code or creating commits
- modifying files outside explicitly mentioned scope
- adding abstractions or changing existing behavior
- running full test suites instead of targeted tests

steering recovery

## after receiving steering

1. acknowledge the correction explicitly
2. do NOT repeat the corrected behavior
3. if pattern recurs (2+ steerings for same issue), ask user for explicit preference
4. track common corrections for this user (flags, file locations, scope boundaries)

thread health monitoring

## thread health indicators

healthy signals:
- approval:steering ratio > 2:1
- steady progress with occasional approvals
- spawning subtasks for parallel work

warning signals:
- ratio drops below 1:1
- 100+ turns without resolution
- multiple consecutive steerings
- user messages getting longer (frustration signal)

action when unhealthy:
- pause and summarize current state
- ask if approach should change
- offer to spawn fresh thread with lessons learned

prompting best practices

## effective user patterns (learned from high performers)

1. terse messages + follow-up questions > verbose context dumps
2. "OK, and what is next?" keeps agent planning visible
3. explicit approvals ("ship it", "commit this") provide clear checkpoints
4. early handoffs (≤10 turns) often mean task mismatch, not failure
5. marathon threads (50+ turns) work for focused domains, not scattered work

oracle usage

## oracle usage

DO use oracle for:
- planning before implementation
- architecture decisions
- code review pre-merge
- debugging hypotheses

DON'T use oracle as:
- last resort when stuck (too late)
- replacement for reading code
- magic fix for unclear requirements

anti-patterns to avoid

1. premature action

acting before user confirms intent. triggers “wait…” interrupts.

signals: running tests immediately, pushing without review, choosing file locations without asking

fix: ask once before taking significant actions

2. scope creep

making changes beyond what user asked.

signals: “full test suite instead of targeted tests”, adding unwanted abstractions, changing preserved behavior

fix: ask before expanding scope. “should I also…?“

3. forgetting flags

repeated failure to remember user-specific preferences.

signals: “you forgot -run=xxx AGAIN”, benchmark flags, filter params

fix: track per-user preferences, reference in context

4. oracle as panic button

reaching for oracle only when already stuck.

signals: oracle usage correlates with frustrated threads, not prevented them

fix: use oracle at planning phase, not recovery phase

5. context overload

long messages that frontload too much context.

signals: 1000+ char messages, agent misses key points, user has to repeat

fix: terse prompts + follow-up questions work better

6. linear copy-paste workflow

treating agent as supplementary info source rather than collaborator.

signals: low steering, low approval, short threads that don’t resolve

fix: iterative refinement cycle, active coordination

7. abandoning prematurely

exiting threads before resolution without spawning follow-up.

signals: <10 turn threads with UNKNOWN status, no thread links

fix: either complete or explicitly spawn continuation

8. marathon without checkpoints

long threads without approval signals.

signals: 100+ turns, low approval:steering ratio, locked in single context

fix: explicit checkpoints every 20-30 turns, consider spawning subtasks

synthesis meta-notes

what we’re confident about

what’s still hunch

research alignment

academic research on human-AI collaboration confirms:

synthesized by frances_petalbell | amp thread analysis pipeline