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 model continuations against the operating mandate and
selects the path that best satisfies the objective, constraints, evidence requirements, and review standards.
Control occurs within the generation path, before drift becomes finished work.
No retraining or model replacement required.
Provider-independent No retraining OpenAI-compatible Path-level evidence
The control point
The control point is
inside generation.
Different layers control different decisions along the model request path.
Assiduity acts inside generation, using an explicit representation of the operating mandate to
evaluate and select the path forward.
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.
Why the control point matters
The control point
changes the outcome.
Generation-time control shapes the work while it is being produced.
Assiduity changes the finished result by influencing the path during generation.
Generation-time control
Shape the work while
choices remain available.
Assiduity evaluates and selects the generation path as the work develops, influencing the finished result before drift becomes embedded in the output.
Post-output review
Inspect the work after
the model completes the path.
Final-output checks determine whether completed work should pass, fail, or be returned for revision after the model has already made the choices that produced it.
Assiduity influences which path becomes the finished work.
Architecture: Equilibrium-Constrained Decoding™
The generation-time
control loop.
Assiduity applies the operating mandate throughout generation, evaluating and selecting the path at each control point as the work develops.
Equilibrium-Constrained Decoding™
The generation control mechanism
01
Mandate
Define the operating mandate
Specify the objective, constraints, exclusions, source requirements, escalation rules, and completion criteria for the workflow.
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 evaluates and selects
Assiduity evaluates candidate continuations against the operating mandate and existing generation path, then selects the continuation that best satisfies the mandate.
04
Repeat
Control continues
Generation, evaluation, and selection repeat where control is needed so that small deviations do not compound into finished work.
05
Evidence
Return the work and evidence
The enterprise receives the 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.
The operating mandate
Translate existing governance into generation-time control.
Assiduity starts with the governance the enterprise already uses: objectives, policies, procedures, requirements, evidence standards, risk limits, review criteria, escalation rules, and definitions of completion. Together, these form the operating mandate for a specific workflow.
A semantic contract is the technical representation of that mandate that Assiduity evaluates while the model generates.
The key translation
The operating mandate makes
existing governance executable.
For a specific workflow, the semantic contract defines the objective, constraints, exclusions, evidence requirements, escalation rules, and completion criteria that Assiduity evaluates while the model works.
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.
Existing governance artifacts Workflow-specific requirements Escalation criteria Completion standards
Adoption
Start with a
governed workflow.
Evaluate generation-time control against a bounded enterprise workflow,
compare controlled and baseline
performance, and expand only after
the operating mandate and evidence meet the required standard.
01
Start here
Define
Select one governed workflow and translate existing policies, procedures, requirements, and review criteria into an operating mandate.
02
Connect
Route the workflow through Assiduity while keeping the application, model provider, and governance process in place.
03
Validate
Compare baseline and controlled outputs, then review trajectory, constraint behavior, completion status, and evidence.
04
Scale
Refine the mandate and extend generation-time control to additional workflows where reliability and evidence matter.
Integration
Works with the enterprise
AI stack already in place.
Assiduity can connect directly to supported model endpoints or use an existing AI gateway as the model-access layer. The gateway manages provider access and routing; Assiduity applies generation-time control to the work produced by the selected model.
Inline or SDK deployment
Add generation-time control through an application workflow or an 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 the organization’s model-access, routing, security, and cost-management layer while adding control over the generation path.
OpenAI-compatible interface SDK deployment Direct endpoint or gateway Provider-independent control
How generation-time control ships
Infrastructure for control, review, and evidence.
Assiduity adds a generation control layer without replacing the underlying model, enterprise application, or model-access infrastructure.
SDK
Define operating mandates and add generation-time control to application workflows for long-form or agentic tasks.
OpenAI-compatible control proxy
Route supported generation through Assiduity while preserving familiar application interfaces and existing model access.
Control dashboard
Inspect controlled runs, baseline comparisons, constraint behavior, generation trajectory, and governance status.
Generation Control Record
Export structured evidence of how control was applied during the run, not only a log of the final output.
Generation Control Record
Preserve evidence of
how control was applied.
Assiduity records what the model did—and how control shaped the path:
where alternatives were evaluated, which continuation was selected,
how the trajectory changed, and whether the operating mandate remained satisfied.
ε trajectory
Track deviation from the operating mandate across the developing generation path.
Constraint behavior
Review whether required concepts were covered, prohibited terms were avoided, and workflow expectations were maintained.
Candidate-selection records
See when the controller evaluated alternatives, which continuation was selected, and how the control decision affected the path.
Escalation and completion status
Preserve escalation signals, stop reasons, pass/fail checks, and run-level governance summaries for review.
Cost-aware control
Apply control where
drift risk rises.
Generation-time control does not require maximum branching at every step.
Assiduity concentrates control where the developing path shows higher drift risk,
reducing unnecessary candidate generation while preserving measurable control benefit.
Assiduity complements model choice rather than replacing it. It improves the operating reliability of model workflows—including smaller or lower-cost models—where long-horizon drift creates avoidable review, rework, and governance burden.
Evaluation workflows
Where to test
generation-time control.
Start where the work is extended, the mandate is explicit, and drift can create
measurable review, rework, or governance cost.
Long-form analysis
Evaluate whether extended summaries, research syntheses, and policy analyses stay tied to the original objective and evidence requirements.
Generative work inside agentic workflows
Apply generation-time control to reports, analyses, recommendations, plans, and other extended outputs produced within multi-step agent workflows.
Regulated work
Produce structured control evidence that supports monitoring, review, governance, and controlled technical evaluation.
Model governance
Give reviewers more than a final answer by exposing trajectory, control decisions, and constraint signals from the generation itself.
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.