Logic and Flow Design
Agent execution in Texor.Cloud is structured around a directed logic graph composed of modular blocks. Each flow is designed as a dynamic pipeline, capable of evaluating real-time inputs, applying nested condition sets, executing parallel branches, and resolving outputs based on deterministic or model-informed logic.
This system allows for the composition of highly adaptive agents capable of handling non-linear, state-aware automation at scale.
Conditional Branching
Logic blocks support conditional forking within the execution graph. Flow control structures such as if-else branches, threshold validators, and boolean conditions allow for routing based on real-time data or evaluation outputs.
Decision trees can be expanded into multi-branch flows where each path can invoke a distinct execution context, complete with its own downstream behavior.
Multi-Layered Logic Pipelines
Flows are not restricted to linear processing. You can construct layered logic systems where AI models, condition trees, and external data validations are composed together in sequence or parallel. Intermediate outputs can be routed, stored, transformed, or scored before proceeding.
This architecture enables decision chaining, fallback logic, and the construction of deeply reactive automation systems without code.
Model-Integrated Logic
AI models are treated as native logic components. You can insert a model into any decision node to classify, generate, evaluate, or rank data in context. Models may operate on structured inputs, raw content, or aggregated multi-source signals and return outputs to be interpreted downstream by logic blocks.
This transforms static flows into semi-autonomous agents capable of interpreting and reacting in more abstract or human-like ways.
Fault Tolerance and Recovery Paths
Agents are designed to support conditional fallbacks and recovery logic. Flows can be configured to resolve errors, reroute execution, or trigger alternate processes on failure, timeout, or invalid return types.
This makes the system suitable for real-world tasks where failure must be handled gracefully without halting the overall process.
Logic Reusability
Every logic configuration is modular. Blocks can be reused across agents, parameterized with scoped context, or saved as reusable logic fragments. This makes it possible to standardize and scale complex automations across teams or systems without duplication.
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