01 / RESEARCH NOTECoordination
When does an agent actually need another agent?
The overlooked cost of delegation in tool-heavy workflows.
THE FIELD NOTES OF AGENTIC INTELLIGENCE
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Research moves fast. Understanding takes time. We unpack the mechanisms, evidence, and open questions behind agentic AI—one consequential idea at a time.
FIG 01. / CONVERGENCE
EXPLORATION → UNDERSTANDING
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FEATURED READING · Nov 1, 2026
When does an agent actually need another agent?
The overlooked cost of delegation in tool-heavy workflows. Explore what the experiments reveal—and what they still don't.
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THE RESEARCH LIBRARY
Discover the evidence.
Readings selected for their educational value, technical insight, and opportunities for critical thinking.
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02 / RESEARCH NOTEEvaluation
The harness may matter as much as the model.
Why changing the agent loop can swing the result without changing model weights.
03 / RESEARCH NOTEReliability
The bug you introduce today may sabotage tomorrow.
Long-horizon coding benchmarks expose inherited defects.
04 / RESEARCH NOTEVerification
Passing tests are not necessarily good tests.
Behavioral adequacy is a different objective from test execution.
05 / RESEARCH NOTESafety
Knowing a tool action is risky is not enough.
Safety should be measured by actual system effects.
06 / RESEARCH NOTECoordination
What if agent teams could reorganize mid-task?
Adaptive roles can sometimes escape an optimization plateau.
07 / RESEARCH NOTEVerification
What counts as evidence that an agent finished?
Observability is not the same as a trustworthy completion claim.
08 / RESEARCH NOTEEngineering
Why code-generation agents need explicit contracts.
File ownership and compile checks can improve buildability.
09 / RESEARCH NOTECoordination
Agents can form informal rules around scarce compute.
Resource allocation becomes a coordination problem with surprising behaviors.
10 / RESEARCH NOTECoordination
Agents cooperate better when they can see each other.
An observable shared workspace may outperform silent parallelism.
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