The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
When multiple goals compete for the same scarce resource, match the allocation mechanism to dependency structure—use priority queue when importance differs, rotation when all are equal, and time-slicing when multiple need access within the same period.
For each handoff between agents or pipeline stages, specify three components—defined output format, explicit expectations, and return protocol—to prevent ambiguous handoffs from creating bottlenecks.
Never add agents to sequential reasoning tasks—distribute additional agents only to genuinely parallel workstreams where coordination overhead is measurably less than throughput gain.
Set an explicit coordination budget as a percentage of total available hours (15-25% for most knowledge work), and require any new coordination mechanism to fit within that budget or displace an existing one.
Invest in implicit coordination mechanisms—shared mental models, conventions, templates, and routines—over explicit communication channels, because implicit coordination scales without consuming bandwidth as agent count grows.
For beneficial emergent behaviors, protect the conditions that produced them—keeping agents active, maintaining shared context, and adding lightweight observation—rather than formalizing the pattern into an explicit rule.
When performing ecosystem health assessments, examine agent pairs for three specific failure modes: conflicting outputs, throughput mismatches between producer and consumer, and resource competition for the same limited capacity.
Schedule agent addition reviews 7-14 days after deployment to verify whether actual coordination cost matches pre-addition estimates, removing the agent if cost exceeds estimate by more than 50%.
Before retiring any agent, map all dependencies by identifying what consumes its output, what constraints it enforces, and what failures it masks—then explicitly reroute, replace, or accept each dependency gap.
When removing an agent, execute graduated shutdown by reducing frequency or scope before full elimination, monitoring for hidden dependencies during the transition period.
During 30-minute coordination reviews, answer four diagnostic questions with evidence: which agents produced output, did outputs reach intended consumers, what was the coordination-to-work time ratio, and where did agents actively interfere.
For agents with weak coordination, design hand-off protocols by specifying what information transfers, in what format, and at what trigger point—then practice the hand-off deliberately in the next three executions.
Calculate your attention allocation by categorizing each task as ONLY ME (requires unique judgment), COULD DELEGATE (someone/something else can do it at 80%+ quality), or SHOULD NOT EXIST (adds no value), then delegate or eliminate everything outside ONLY ME to reclaim attention for highest-value work.
When your ONLY ME time falls below 50% of working hours, you have a delegation deficit requiring immediate correction, because spending the majority of your highest-value resource on work that doesn't require it violates the constraint optimization principle.
Before delegating a task, verify it is not ONLY ME by default rather than by necessity—if the task requires your unique judgment only because you've never built documentation, systems, or relationships to make it delegable, it's a disguised delegation candidate.
Default to the delegation hierarchy sequence: first ask 'can a system handle this?', then 'can a system handle 80% with a person handling exceptions?', and only then 'does this require full human judgment?'—to prevent systematic under-investment in systems.
When modifying organizational processes, surface embedded schemas first to distinguish processes encoding hard-won lessons (whose removal reintroduces risk) from processes encoding obsolete assumptions (whose removal reduces waste).
When reclaiming a delegated decision that scored 2+ on the non-delegable filter, restructure by retaining the non-delegable core (judgment, value trade-off, contextual interpretation) while re-delegating execution (research, analysis, implementation) to preserve leverage without losing identity.
Test specification completeness by asking whether a competent stranger with relevant skills but zero context could produce an acceptable result from your specification alone—if not, the missing information must be externalized before delegation.
Design verification as three independent layers—continuous signals (daily metrics), periodic samples (weekly/monthly spot-checks), and infrequent structural audits (quarterly full reviews)—with each layer optimized for different failure detection at different resource costs.
Make verification checkpoints transparent to delegates by communicating what you will check, when you will check it, and what standards apply, because hidden monitoring functions as surveillance while transparent verification functions as professional collaboration.
When a verification signal degrades, escalate to deeper sampling; when sampling reveals a pattern, trigger a structural audit; when an audit reveals a structural problem, modify the delegation itself—this creates a cascading response protocol where each verification layer can activate the next.
Extend verification intervals when a delegate produces consistent quality outputs over time, and tighten intervals when errors surface, treating trust as a dynamic variable calibrated by accumulated evidence rather than a fixed initial condition.
For high-stakes AI outputs, adopt a three-tier verification intensity: skim for low-stakes brainstorming, spot-check key claims for medium-stakes communications, and verify every substantive claim for high-stakes published or production content.