Generation-time control
Shape the work as it develops.
Measure drift, compare alternatives, and select the stronger path before deviation compounds.
Generation-time control
Keep AI on mandate while it works.
Assiduity turns objectives, constraints, evidence rules, and escalation boundaries into generation-time control.
Set acceptable deviation. Assiduity steers the path, adds control when needed, and escalates residual risk for human review.
No retrainingControl at inference.
Model independentUse supported providers.
OpenAI-compatibleOne endpoint change.
Path evidencePreserve how control was applied.
Why the control point matters
Control the path before it becomes the answer.
Final-output review inspects completed work. Assiduity acts earlier—while better continuations still remain available.
Post-output review
Inspect the work after completion.
Pass, fail, escalate, or revise after the model has already made the choices that produced the result.
Assiduity influences which path becomes the finished work.
The operating mandate
Make governance executable.
Translate the workflow’s existing objectives, rules, evidence standards, escalation thresholds, and completion criteria into a semantic contract.
Objective
What the work must accomplish.
Constraints
What it must preserve or include.
Exclusions
What it must not infer or introduce.
Evidence
What must support claims or flags.
Escalation
When human judgment is required.
Completion
What “done” means.
semantic-contract · vendor-review
# illustrative example
objective: flag non-standard indemnity clauses
constraint: cite the source clause for every flag
exclusion: do not infer unstated intent
evidence: link every flag to clause text
escalation: liability > $5M → human review
completion: all clauses reviewed and sourced
Contracts are derived from the enterprise’s own policies, procedures, and review standards.
Risk-calibrated control
Set tolerance. Spend control where risk rises.
The enterprise sets acceptable deviation. Assiduity uses that threshold to allocate compute and human attention.
Set tolerance
Define acceptable deviation (ε) for the workflow.
Add control
Use sparse branching when the path moves outside the preferred range.
Escalate residual risk
Route unresolved deviation to human review.
Equilibrium-Constrained Decoding™
The generation-time control loop.
The mandate defines what the work must satisfy. Risk tolerance defines how far it may deviate.
Equilibrium-Constrained Decoding™
Generation control mechanism
01
Mandate
Define
Set the mandate and acceptable deviation.
02
Generate
Propose
The model generates candidate continuations.
03
Evaluate
Measure + select
Assiduity measures deviation and selects the strongest continuation.
04
Control
Steer or escalate
Add machine effort or route residual risk to review.
05
Evidence
Return
Deliver the work with its control record.
Repeat as the path develops.
Built for agentic scale
Represent the mandate—not every failure.
Agentic systems create many possible paths. Assiduity evaluates those paths against the intended state instead of enumerating every exception.
The missing control point
Different layers control different decisions.
Gateways manage the call. Guardrails enforce boundaries. Monitoring observes behavior. Assiduity controls the developing path.
Generation control During generation
Assiduity
Controls the developing path.
Evaluates candidate continuations against the operating mandate and selects the path forward.
Boundary control Input · Output · Action
Guardrails
Control what may pass.
Allow, block, or escalate against defined rules.
Behavioral control Across runs
Agent monitoring
Improves recurring behavior.
Detects repeated failures and feeds what the organization learns into future workflows.
Operational control Access · Routing · Resilience
AI gateways
Control the model call.
Manage providers, routing, credentials, fallbacks, latency, and spend.
Evaluation Model · Output
Evaluation
Measures quality.
Scores models and completed outputs.
Development Prompts · Chains · Workflows
Development tooling
Debugs the application.
Inspects prompts, agents, traces, and workflow logic.
Infrastructure Systems · Performance
Infrastructure monitoring
Monitors system health.
Tracks latency, availability, errors, and throughput.
The model proposes continuations. Assiduity decides the path.
Generation Control Record
Keep evidence of how control was applied.
Preserve the trajectory, constraints, selections, escalation signals, and completion state behind the finished work.
Where it fits
One control layer. The rest of the stack stays in place.
Keep the application, provider infrastructure, and supported model endpoint. Add Assiduity between the workflow and the model.
Operating Mandate
Policies · procedures · requirements · review standards
Enterprise App / Agent
Assiduity Generation-Time Control
Model Endpointsupported provider
Controlled OutputWork kept on mandate
Path EvidenceDrift · constraints · selections · escalation
Provider-independent No retraining OpenAI-compatible proxy One endpoint change
Integration
Use the stack already in place.
Connect through the SDK or OpenAI-compatible proxy. Keep existing model access, routing, security, and provider relationships.
Inline or SDKAdd control without rebuilding the workflow.
No weight changesApply control during inference.
Gateway compatiblePreserve routing and model-access infrastructure.
Adoption
Start with one governed workflow.
01Define
Translate existing requirements into the mandate.
02Connect
Route the workflow through Assiduity.
03Validate
Compare baseline and controlled performance.
04Scale
Expand where control earns its place.
Where to start
Start where drift creates measurable cost.
Choose extended work with explicit requirements and meaningful review or rework cost.
FAQ
Frequently asked questions
What is Assiduity?
Assiduity provides generation-time control for enterprise AI. It steers developing work toward the operating mandate and returns path-level evidence with the result.
What is generation-time control?
It evaluates and influences the developing generation path while alternative continuations remain available, rather than waiting for the final output.
What is an operating mandate?
It defines the workflow’s objective, constraints, exclusions, evidence rules, escalation thresholds, and completion criteria.
What is Equilibrium-Constrained Decoding?
Equilibrium-Constrained Decoding™ is Assiduity’s patent-pending generation-control approach. It evaluates candidate continuations against the mandate and selects the strongest path forward.
How is Assiduity different from observability?
Observability shows what happened. Assiduity acts during the current generation and records how control changed the path.
How is Assiduity different from guardrails?
Guardrails enforce rules at defined boundaries. Assiduity evaluates possible next directions and steers the developing work toward the broader mandate.
Does Assiduity replace or retrain the model?
No. The selected model remains the generative engine. Assiduity applies control during inference without changing model weights.
What is the Generation Control Record?
It preserves structured evidence from the run, including trajectory, constraint state, selection activity, escalation signals, and completion status.
Can Assiduity support smaller or lower-cost models?
It can improve operating reliability in suitable workflows, but it does not make every smaller model equivalent to a frontier model.
Protected evaluation
See generation control in action.
Compare baseline and controlled output, then inspect the path evidence behind the run.