Building Reliability Into Decision Intelligence: Synachron Development Update

Synachron is moving from simply running synthetic-population simulations toward measuring how much confidence decision-makers should place in the agents and evidence behind each result.

The latest development milestone is Phase 1 of the Agent Reliability Framework. The core data structures, reliability policy, active policy setting, and metric definitions are now implemented in the live Synachron platform.

The framework evaluates reliability across seven weighted components: accuracy, stability, calibration, consistency, robustness, provenance, and quality. These components feed an overall reliability assessment rather than allowing a single strong metric to stand in for evidence quality as a whole.

The current policy gives the greatest weight to accuracy at 35 percent, followed by stability at 20 percent and calibration at 15 percent. Consistency and robustness each contribute 10 percent, while provenance and quality contribute 5 percent each.

More important than the weights is the evidence hierarchy behind them. Synachron now distinguishes between insufficient evidence, diagnostic evidence, replicated synthetic evidence, retrospective human evidence, and prospective human evidence. Each tier places a ceiling on the reliability classification that can be displayed.

That means synthetic stability alone can never make an agent or prediction “validated.” A Validated classification requires prospective human evidence as well as an overall reliability score of at least 85. The purpose is to prevent the platform from turning internal consistency into an unsupported scientific claim.

This work builds on the broader Validation, Verification and Benchmarking Framework already under development. In the ESS11 Greece blind-replication programme, ground truth is sealed, benchmark structures are locked, simulations are repeated, and run stability is measured before results are compared with human survey data. The current mean option-share standard deviation is approximately 2.17 percentage points against a 2.0-point pass threshold, so the stability gate is still classified as WARN rather than PASS. Aggregate and segment accuracy also remain outside the current acceptance gates.

We consider that an important result, not something to hide. A validation system is useful only if it can reject its own outputs when the evidence is not strong enough.

The next engineering stage is the Agent Reliability calculation engine. It will generate versioned reliability assessments from validation results, replication runs, quality flags, certification records, and execution provenance. Reliability views will then be added to agent profiles, studies, simulation runs, and reports, with visible evidence-tier caps and explanations for any blocking flags.

Beyond that, the roadmap moves toward continuous prediction monitoring and recalibration, the Decision Copilot, the Evidence Agent, automated data intake, and the Synachron Accelerator Library. These are roadmap items, not features we are claiming as completed today.

The architectural principle remains unchanged: Synachron is one Decision Intelligence Engine. Data, evidence, digital twins, agents, scenarios, simulated behaviour, prediction, validation, decision, and action should remain connected through one auditable system rather than becoming a collection of unrelated AI tools.

The objective is straightforward: not merely to produce a simulation, but to make the reliability, assumptions, provenance, uncertainty, and validation status of that simulation visible enough to support a real decision.