Generation control plane Production · During generation
Assiduity
Controls the developing generation path.
Evaluates candidate continuations against the operating mandate and selects the path forward within the current generation.
Generation-time control
Keep AI on mandate during generation.
Assiduity evaluates developing generation against the operating mandate and selects paths that preserve the objective, constraints, evidence requirements, escalation rules, and completion standard.
The enterprise sets acceptable deviation. Assiduity applies control while choices remain and can escalate unresolved deviation for human review. No retraining or model replacement required.
Provider-independent No retraining OpenAI-compatible Path-level evidence
Why the control point matters
The control point
changes the outcome.
Final-output review decides what to do with completed work. Generation-time control influences which path becomes that work in the first place.
Generation-time control
Shape the work while
choices remain available.
Assiduity evaluates and selects the generation path as the work develops, before small deviations compound into the finished result.
Post-output review
Inspect the work after
the path is complete.
Final-output checks determine whether completed work should pass, fail, escalate, or return for revision after the model has already made the choices that produced it.
Assiduity influences which path becomes the finished work.
The operating mandate
Translate existing governance
into generation-time control.
Assiduity starts with the governance the enterprise already uses: objectives, policies, procedures, evidence standards, escalation rules, and definitions of completion.
A semantic contract represents that workflow-specific mandate so it can remain active while the model generates.
The key translation
Make the operating mandate executable.
The semantic contract defines what the work must accomplish, preserve, avoid, evidence, escalate, and complete.
Objective
What the AI system is trying to accomplish.
Constraints
What it must preserve, include, or satisfy.
Exclusions
What it must not infer, omit, or introduce.
Evidence
What support is required for claims, flags, or recommendations.
Escalation
When ambiguity, risk, or threshold breaches require human review.
Completion
What “done” means for the governed workflow.
semantic-contract · vendor-review
# illustrative example
objective: flag non-standard indemnity clauses
constraint: cite the source clause for every flag
exclusion: never infer intent the document does not state
evidence: each flag links to exact clause text
escalation: liability > $5M routes to human review
completion: all clauses reviewed, flags sourced, summary approved
Real semantic contracts are derived from the enterprise’s own policies, procedures, and review standards.
Risk-calibrated control
Set the tolerance.
Allocate control accordingly.
Different workflows tolerate different levels of deviation from the operating mandate. The enterprise defines the acceptable range; Assiduity uses it to allocate machine control and human review.
Set the tolerance
Define the acceptable level of deviation (ε) for the workflow based on its risk, consequence, and review requirements.
Spend compute where needed
As deviation rises, sparse branching can evaluate additional continuations and steer the generation back toward the mandate without branching everywhere.
Escalate residual risk
If the path remains outside the acceptable range after control, the workflow can escalate to human review rather than silently passing unresolved deviation forward.
Risk tolerance determines where to spend compute—and when to spend human attention.
Architecture: Equilibrium-Constrained Decoding™
The generation-time
control loop.
The mandate defines what the work must satisfy. Risk tolerance defines how much deviation is acceptable. ECD uses both while the generation path develops.
Equilibrium-Constrained Decoding™
The generation control mechanism
01
Mandate
Define mandate and tolerance
Specify what the work must satisfy and how much deviation the workflow can accept before additional control or escalation is required.
02
Generate
The model generates alternatives
The selected model remains the generative engine and produces candidate continuations for the developing work.
03
Evaluate
+ select
+ select
Assiduity measures and selects
Assiduity evaluates deviation from the mandate, compares candidate continuations where control is warranted, and selects the path that best restores fidelity.
04
Repeat
Control or escalate
Sparse branching adds machine effort where deviation warrants it. If residual deviation remains outside tolerance, the workflow can escalate to human review.
05
Evidence
Return the work and evidence
The enterprise receives completed output together with a structured record of trajectory, constraints, control decisions, escalation signals, and completion status.
The loop repeats as the generation path develops.
Built for the scale of agentic AI
Scale control by
representing the mandate.
Agentic AI creates many possible paths. Exception-based controls scale by adding rules and failure patterns; Assiduity evaluates those paths against a representation of the intended state.
The goal is not to enumerate every failure. It is to preserve the mandate across many possible outputs.
The missing control point
Different layers control
different decisions.
Gateways manage the call. Guardrails control boundaries. Monitoring observes behavior. Assiduity controls the developing generation path.
Boundary control plane Prompt · Output · Action
Guardrails
Control what may pass.
Apply defined rules at input, output, or action boundaries to allow, block, or escalate content and behavior.
Behavioral control plane Production · Across runs
Agent monitoring
Improves recurring production behavior.
Detects behavioral failures across interactions and feeds what the organization learns into future policies, prompts, and workflows.
Operational control plane Model access · Routing · Resilience
AI gateways
Control the conditions of the model call.
Manage provider access, model selection, routing, credentials, fallbacks, usage, latency, and spend.
Evaluation plane Model · Output quality
Evaluation
Measures model and output quality.
Produces quality scores, test results, and evaluation signals showing how well a model or completed output performed.
Development plane Prompts · Chains · Workflows
Development tooling
Debugs how AI applications are built.
Helps teams inspect prompts, chains, agents, traces, and workflow logic during development and testing.
Infrastructure plane Systems · Performance
Infrastructure monitoring
Monitors system health.
Tracks latency, availability, errors, throughput, and other operating signals across the underlying infrastructure.
The model proposes the continuations. Assiduity decides the path.
Generation Control Record
Preserve evidence of
how control was applied.
Assiduity preserves the trajectory, constraint state, control decisions, escalation signals, and completion status behind the finished work.
ε trajectory
Track deviation from the operating mandate across the developing generation path.
Constraint behavior
Review whether required concepts, prohibited terms, evidence conditions, and workflow expectations remained satisfied.
Candidate-selection records
See when alternatives were evaluated, which continuation was selected, and how the control decision affected the path.
Escalation and completion
Preserve escalation signals, stop reasons, pass/fail checks, and run-level governance summaries for review.
Where it fits
A control layer between
the workflow and the model.
Assiduity translates the enterprise operating mandate into generation-time control while preserving the application, model-access infrastructure, and supported model endpoints.
Operating Mandate
Derived from policies, procedures,
requirements, and review standards
Enterprise App / Agent
Assiduity Generation-Time Control
Model Endpointsupported provider
Controlled OutputWork kept on mandate
Path-Level EvidenceConstraint behavior · Drift trajectory
Control decisions · Governance signals
Control decisions · Governance signals
Existing governance artifacts Provider-independent No retraining OpenAI-compatible proxy One API endpoint change
Integration
Works with the enterprise
AI stack already in place.
Connect directly to supported model endpoints or keep the existing AI gateway for provider access and routing. Assiduity adds control over the selected model’s generation path.
Inline or SDK deployment
Add generation-time control through an application workflow or OpenAI-compatible control proxy rather than rebuilding the system.
No weight modification
Control is applied during inference, so teams can evaluate behavior without retraining or changing model weights.
Existing gateway compatible
Preserve model access, routing, security, and cost management while adding control over the generation path.
Adoption
Start with one
governed workflow.
Start with one bounded workflow. Compare baseline and controlled performance, review the evidence, and scale only if the result meets the required standard.
01
Start here
Define
Select one governed workflow and translate existing requirements and review criteria into an operating mandate.
02
Connect
Route the workflow through Assiduity while keeping the application, provider, and governance process in place.
03
Validate
Compare baseline and controlled outputs, then inspect trajectory, constraints, completion status, and evidence.
04
Scale
Refine the mandate and extend control only to workflows where reliability and evidence justify the added layer.
Where to evaluate it
Start where drift creates
measurable enterprise cost.
The best initial workflows are extended, governed by explicit requirements, and expensive to reread or repair when the model quietly misses part of the mandate.
Long-form analysis
Research syntheses, policy analyses, and reports with explicit coverage and evidence requirements.
Agent-generated work
Reports, recommendations, plans, and other extended outputs produced inside multi-step agent workflows.
Regulated work
Workflows where factual requirements, evidence standards, escalation rules, or defined scope must remain active.
Model governance
Workflows where reviewers need evidence from the generation path, not only a score on the finished answer.
FAQ
Frequently asked questions
What is Assiduity?
Assiduity provides generation-time control for enterprise AI. It evaluates possible continuations while the model is generating, selects the path that stays closest to the operating mandate, and returns the completed work with path-level evidence.
What is generation-time control?
Generation-time control is a form of runtime control applied inside the model’s generation process. It evaluates and influences the developing path while alternative continuations remain available, rather than waiting to inspect only the final output.
What is an operating mandate?
The operating mandate defines the objective, constraints, exclusions, evidence requirements, escalation rules, review standards, and completion criteria for a governed workflow. A semantic contract is the technical representation Assiduity evaluates while the model generates.
What is Equilibrium-Constrained Decoding?
Equilibrium-Constrained DecodingTM is Assiduity’s patent-pending generation-control approach. At a high level, it evaluates candidate continuations against the operating mandate and developing path, then selects the continuation that best satisfies the mandate.
How is Assiduity different from observability?
Observability shows what a system produced and how it behaved. Assiduity acts inside the current generation, influencing which continuation becomes part of the finished work while also preserving evidence of the control decisions.
How is Assiduity different from guardrails?
Guardrails typically allow, block, or escalate content and actions against defined rules at input, output, or action boundaries. Assiduity evaluates possible next directions during generation and steers the developing work toward the broader operating mandate.
Does Assiduity replace the model?
No. The selected model remains the generative engine. Assiduity sits between the enterprise workflow and supported model endpoints as a generation-time control layer.
Does it require model retraining?
No. Assiduity applies control during inference without modifying model weights. Teams can evaluate, refine, or remove the control layer without retraining the underlying model.
What is the Generation Control Record?
The Generation Control Record preserves structured evidence of how control was applied during a run, including trajectory, constraint behavior, candidate-selection activity, escalation signals, completion status, and run-level governance summaries.
Can Assiduity support smaller or lower-cost models?
Assiduity can improve the operating reliability of smaller and lower-cost models by applying control during generation. It does not make every smaller model equivalent to a frontier model, but it can reduce avoidable review, rework, and governance burden in suitable workflows.
What does the protected demo show?
The demo compares baseline generation with ECD-controlled output and displays the path-level evidence behind the run, including constraint satisfaction, ε trajectory, candidate-selection activity, and governance status.
Protected evaluation
See generation control in action.
The protected demo compares baseline generation with ECD-controlled output, including constraint satisfaction, ε trajectory, candidate-selection activity, and the Generation Control Record.