AI workflow design
Identify where AI should and should not sit in the process, based on risk, value, and operational reality.
Enterprise AI architecture & engineering
Enterprise AI that actually gets into production.
Agentek designs, governs, and deploys AI workflows across real systems, real controls, and real operational constraints — built for regulated enterprises.
The problem
Most AI initiatives fail between pilot and production.
The issue is rarely the model. It is the workflow, the controls, the data, the integrations, and the operating model around it. Agentek helps enterprises solve those real-world constraints so AI can move beyond slideware and isolated demos.
What we do
01 / Workflow
02 / Controls
03 / Delivery
Strategy, control design, and implementation shaped for enterprise reality.
Architecture and governance
Design the control layer around AI with human review, escalation paths, observability, and clear decision boundaries.
Implementation and scale
Move from concept to live deployment across enterprise systems, with Agentek-led delivery and specialist implementation support where needed.
Use cases
→ Operations
→ Compliance
→ Servicing
→ Knowledge
→ Review
→ Assurance
Designed for workflow-heavy, regulated, operationally complex environments.
Document intake and triage
Automated classification, extraction, and routing of inbound documents with human review thresholds for exceptions.
Client onboarding and KYC
Agent-led onboarding workflows with compliance-bounded controls and full audit trail for regulated industries.
Claims and servicing workflows
Multi-step claims processing with deterministic SOPs, escalation paths, and integration into case management systems.
Knowledge-grounded operations support
Retrieval-grounded agents that answer operational queries from your own knowledge base, with confidence thresholds and citations.
Compliance and review workflows
AI-assisted review with human-in-the-loop sign-off, designed for environments where audit and explainability are non-negotiable.
Workflow assurance and escalation
Monitoring, exception handling, and escalation logic built in from day one — not bolted on later.
Platform / Control Layer
Agentek Control Layer for production AI agents.
A delivery platform for designing, governing, testing, observing, and safely operating AI agents in regulated workflows.
Agentek helps enterprises build AI agents, but our platform is the control layer that makes those agents safe, observable, testable, and production-ready.
Built around reusable control patterns for regulated agent delivery, including workflow templates, guardrails, evals, observability, audit trails, and client-controlled deployment patterns.
Reusable regulated workflows
Starting points for document intake, KYC, claims, compliance review, and operations support.
Policy-aware boundaries
Reusable controls for refusal logic, escalation rules, data handling, and model behaviour limits.
Escalation paths by design
Confidence thresholds, approval queues, exception routing, and reviewer decision capture.
Test sets and release gates
Golden tasks, adversarial prompts, policy checks, regression tests, and business-risk thresholds.
Dashboards and traces
Visibility across prompts, retrieval, tool calls, model outputs, reviewer actions, and failures.
Access and data boundaries
Templates for identity, permissions, tenant separation, data minimisation, and sensitive-system access.
Every agent decision recorded
Traceable evidence for what happened, why it happened, who reviewed it, and what changed.
Client-ready production design
Deployment patterns, integration maps, control model, eval plan, and operating model for delivery.
Detailed case study
Related pattern / Secure knowledge
Related pattern / Release assurance
Related pattern / Operational monitoring
Regulated document intake with a production control layer.
A representative example of how Agentek turns a workflow-heavy AI idea into a controlled operating system for real enterprise teams.
Representative case study / Regulated operations
Observable document triage and exception handling
A regulated operations team receives high volumes of inbound documents across email, portal uploads, and internal queues. The goal is to classify each item, extract key fields, route the case, and escalate exceptions without losing auditability or control.
The situation
Manual triage creates delays, inconsistent routing, and limited visibility into why decisions were made. Teams need AI assistance, but they also need a defensible record of each classification, extraction, confidence score, exception, and reviewer decision.
Agentek response
Agentek designs the workflow architecture, control model, and delivery path before implementation. The system separates deterministic rules from model-led reasoning, adds human approval at defined risk thresholds, and instruments every step for audit and improvement.
01 / Intake
Ingest and normalise
Capture source, document type, sender context, metadata, permissions, and case identifiers before any AI step runs.
02 / Analyse
Classify and extract
Use structured prompts, schemas, retrieval, citations, and validation checks to produce traceable outputs.
03 / Control
Route or escalate
Apply confidence thresholds, policy rules, and human review for exceptions, high-risk cases, or missing evidence.
04 / Operate
Monitor and improve
Track failures, reviewer overrides, prompt changes, eval results, and operational metrics across the workflow.
Observability
End-to-end traceability
Every document receives a trace linking source content, prompt context, model output, tool calls, reviewer decisions, and final routing.
Security
Permission-aware access
Identity, document permissions, data minimisation, tenant separation, and sensitive-field handling are built into the architecture.
Guardrails
Policy-bounded decisions
The AI can recommend, classify, and draft outputs, while deterministic policy checks decide when it must stop or escalate.
Evals
Measured release gates
Golden document sets, edge cases, adversarial inputs, and reviewer feedback become regression tests before production changes ship.
What the client gets
A production-ready operating model, not just a demo.
- Workflow architecture covering intake, extraction, routing, escalation, audit, and system integration.
- Control design for human review, confidence thresholds, model boundaries, and policy enforcement.
- Evaluation suite covering accuracy, retrieval quality, policy adherence, edge cases, and regression risk.
- Operational dashboards and trace data so teams can improve the workflow after launch.
Permission-aware operations assistant
Retrieval-grounded answers that respect user access, cite approved knowledge, block leakage paths, and escalate low-confidence or high-risk queries.
Evaluation gate for workflow agents
Golden tasks, adversarial prompts, tool-use failures, policy checks, and release thresholds for agentic workflow changes.
AI workflow observability
Dashboards and traces across prompts, retrieval, tool calls, reviewer decisions, failure modes, and production usage.
Why Agentek
Built for enterprise reality, not AI theatre.
Agentek is strongest where workflows, controls, systems, and operating model all matter at once. That is where most AI projects slow down, and where structured design makes the difference.
Architecture first
We start with the workflow, controls, data, and operating model. Not a random AI demo.
Governed by design
Human-in-the-loop review, confidence thresholds, auditability, and escalation are designed in from day one.
Built for enterprise reality
AI has to work with existing systems, fragmented data, compliance constraints, and real operating teams.
Outcome-led delivery
We focus on production use, measurable impact, and operating models that can be sustained after launch.
How we deliver
One accountable front door from discovery to deployment.
Agentek leads the architecture, governance, and client outcome. Where it helps execution, we bring in selected specialist implementation partners under an Agentek-led delivery model.
// Delivery ecosystem
Agentek leads architecture, governance, and outcome. Implementation is delivered through selected specialist engineering partners under Agentek's accountability. The client relationship and architectural control stay with Agentek.
Prioritise the workflow
Define the business problem clearly and identify where AI will create measurable operational impact.
Architect the controls
Map controls, data, integration points, and human decision boundaries before any code is written.
Ship into real systems
Implement into production with Agentek leading delivery and specialist engineering support where useful.
Measure and scale
Refine, govern, and scale once the workflow is live, with monitoring and operating model in place.
Proof of fit
// Regulated
// Architecture
// Governance
Credibility for complex operational environments.
Regulated industry experience
Delivery experience in environments where audit, compliance, and operational resilience are non-negotiable. Legal, financial services, and other regulated workflows.
Production-grade architecture
Built on LangChain, LangGraph, vector stores, and major cloud platforms. Deployable into client-controlled environments for GDPR and data residency compliance.
Governed by design
Human-in-the-loop, confidence thresholds, escalation paths, and audit trails built into every workflow from the architecture stage. Not retrofitted afterwards.
// Technology foundation
LangChain
LangGraph
OpenTelemetry
Evals
Guardrails
AWS
Azure
Google Cloud
Contact
Start with the workflow, not the hype.
If you are trying to move AI from idea to operation, Agentek can help shape the use case, define the control model, and design the path to production.
// Phone
+44 7534 524985
// Request a call
30-minute intro call
Use the form →
// Best for
Enterprise AI strategy, workflow design, and implementation planning