Multi-Agent Orchestration
The Problem
Each agent optimizes its local task. No agent knows what the others concluded. One incorrect conclusion propagates through the swarm, amplified at every hop because downstream agents treat upstream inferences as ground truth. By the third hop, the original error is difficult to trace without explicit coordination.
How Ejentum Solves It
One API call per agent forces each node in the swarm to calibrate confidence, detect belief propagation errors, and verify cross-agent consistency. Local optimization cannot ignore global coherence.
The Failures
- 01
The Pattern
Agents optimize their local task without visibility into what other agents have concluded
Why It Happens
The transformer architecture provides no native cross-instance memory. Shared state requires external scaffolding (memory buses, registries, synchronization protocols) that must be engineered into the system, not assumed. Without it, local optimization proceeds without global awareness.
The Resolution
SI-008Multi-Agent Synergy OptimizerDetects emergent cooperation patterns and aligns agents toward collective utility, preventing local optimization from producing globally incoherent outputs.
Supported byMC-024 Cross-Pillar Consistency Guardian - 02
The Pattern
No inter-agent trust calibration: each agent accepts inputs without knowing the confidence of the source
Why It Happens
Agent outputs arrive as text. There is no metadata channel carrying confidence scores, reasoning traces, or epistemic state. The receiving agent treats all inputs as equally reliable.
The Resolution
MC-041Inter-Agent Metacognitive BrokerBrokers belief states and epistemic handshakes between agents, ensuring each agent knows the confidence level and reasoning basis of every other agent it depends on.
Supported byMC-004 Confidence Calibrator - 03
The Pattern
One agent's incorrect conclusion propagates through the swarm, amplified by each downstream agent that treats it as ground truth
Why It Happens
Error propagation is invisible because agents do not distinguish between verified facts and upstream inferences. Each hop treats its input as settled, compounding the original error.
The Resolution
SI-047Belief Network PropagatorPropagates belief updates through the agent network with decay, so downstream agents weight upstream conclusions by their evidential support, not by their position in the chain.
Supported bySI-012 Epistemic Decay Modeler
The Evidence
EjBench, 30 metacognition tasks
Multi-agent failures compound across hops. Scaffold value grows with chain length: measured half-life of 24 steps on ARC-AGI-3. Four abilities distribute self-auditing across the swarm, preventing error amplification.
Task involved detecting overconfidence in a safety argument. Baseline accepted the reasoning at face value. Haki surfaced the unstated assumption and tested the argument against its own criteria. Alternative consideration jumped from 1/3 to 3/3.
Scaffold value compounds with task length. Measured on ARC-AGI-3: scaffold half-life of 24 steps, reasoning quality improving (+0.014 slope) instead of degrading (-0.005 baseline).
Inject one agent in your pipeline with the API. Measure the difference in cross-agent consistency on your next orchestration run.