General
Ship a customer-facing AI feature where wrong answers erode trust: detect output drift, keep a trust baseline (T₀), and repair or block before users see bad results.
Use cases
ReconAI is the control layer for reliable AI agents—detecting, scoring, and repairing failures in multi-step workflows.
- •Agents that produce inconsistent or unreliable outputs across runs
- •Multi-step workflows that break without clear errors or stack traces
- •Production deployments that need validation, retry, and decision audit trails
- •Systems that chain tools and APIs where intent must match each action
Example missions
High-signal first missions to try in Aliethia first contact. Each domain chip in-product loads the same text into your intent field.
Agent teams
Our planner → tool → writer agent chain sometimes confabulates or drifts from the brief after tool calls—validate every step still matches the mission goal and flag handoff breaks early.
Enterprise
Regulated intake bot: one off-domain reply or policy violation is unacceptable—enforce intent echo, block bad actions, and keep a full audit trail for compliance review.
Developer
LLM-assisted code review and codegen in CI: when the model approves a change that would break our API contract or data integrity, I need replay, evidence, and automatic recovery paths.
Operator
On-call agent that triages incidents and opens tickets: I will not enable full Slack/Jira autonomy until trust echo proves every action still aligns with the original incident brief.