Identify intent, user, permissions, and missing context.
Custom Agentic AI Development
AI agents that reason, plan, and act.
We build governed AI agents around your business logic, tools, data, and approval rules — so they work inside production workflows, not just demos.
Action-levelHuman control
TraceableEvery tool call
EvaluatedBefore production
Agent engineering system
Interactive frameworkFrom an AI concept to a governed production agent.
Decision 01
What job should the agent own?
The workflow is broken into triggers, information requirements, decisions, tool actions, approvals, exceptions, and measurable outcomes.
Complete only the steps permitted by the workflow.
Record the result and preserve an audit trail.
Decision 02
Which models, tools, memory, and systems are required?
The model is one component inside a wider architecture containing data access, orchestration, business tools, validation, and user experience.
ExperienceChat · Copilot · Workflow UI · Notifications
Agent layerReasoning · Orchestration · Memory · Tool selection
Enterprise layerCRM · ERP · APIs · Documents · Databases
EvaluationSecurityObservabilityCost
Decision 03
How much autonomy is appropriate for each action?
Read, draft, recommend, update, approve, and financial actions can each have different validation and human oversight requirements.
Answer from approved knowledgeAct automaticallySource required
Update an operational recordValidate firstRule check
Send an external commitmentHuman approvalNamed owner
Handle an unknown exceptionEscalateSafe fallback
Decision 04
How will quality improve after launch?
Production traces, evaluation results, user feedback, failures, latency, and cost are converted into a managed improvement cycle.
Capture tasks, tool calls, failures, latency, and cost.
Measure success, grounding, safety, and regressions.
Refine tools, prompts, retrieval, policies, and UX.
Deploy controlled changes with measurable impact.
Final outcome
A controlled AI agent connecting a real workflow, enterprise tools, permissions, evaluation, and continuous improvement.
How an agent becomes production-ready
Four layers convert an AI demonstration into a controlled operating system.
The service is easier to understand when represented as a progression from the real job to the controls and operating model around it.
Outcome
An agent that can perform useful work while remaining observable, measurable, and governed.
01
Design the workflow before the agent
We define the task, trigger, users, information, decisions, tools, exceptions, and desired outcome.
Current and future workflow mappingHuman and system responsibilitiesSuccess criteria and exception cases
02
Engineer the agent architecture
We connect the right models, knowledge, memory, tools, APIs, and interfaces around the workflow.
Model and orchestration selectionRetrieval, memory, and tool designCRM, ERP, API, and data integration
03
Control every meaningful action
Autonomy is assigned deliberately according to the consequence of each decision or system change.
Validation and permission rulesHuman approval for sensitive actionsFallbacks, audit trails, and escalation
04
Evaluate and improve in production
Quality, safety, latency, user feedback, failures, and cost become part of a managed improvement cycle.
Representative evaluation suitesTracing and operational monitoringControlled optimisation and releases
Generative AI use cases
Different GenAI systems for different business needs.
From grounded knowledge assistants to voice, text, visual, and action-taking agents, we design the right AI system around the information, output, and workflow your business requires.
01
RAG & Knowledge Systems
Ground AI responses in approved documents, databases, and live business sources so teams receive accurate, traceable answers.
02
Voice Generative AI
Create natural voice experiences that listen, understand, respond, and complete approved actions across customer and internal workflows.
03
Text-to-Text GenAI
Transform prompts, conversations, and documents into useful, structured, and brand-aligned business content.
04
Image & Multimodal AI
Combine text, image, and document understanding to automate visual analysis, content production, and richer user experiences.
05
Agentic Workflow Automation
Give AI controlled access to business tools so it can complete multi-step tasks rather than only generate an answer.
06
Custom Copilots & Multi-Agent Systems
Build role-specific copilots or coordinated specialist agents that research, reason, review, act, and hand work off cleanly.
Why Cosnet is different
Built for controlled autonomy, not unchecked automation.
We treat guardrails, permissions, evaluation, and observability as core architecture.
Typical AI agent build
–
Starts with a chatbot interface and adds integrations later.
–
Uses broad system access without action-level permissions.
–
Relies on demo success rather than repeatable evaluation.
–
Provides limited visibility into why an action happened.
Cosnet agent engineering
+
Starts with the workflow, business boundaries, and success measures.
+
Designs least-privilege tool access and human approval by action.
+
Builds automated evaluations for accuracy, safety, and regressions.
+
Traces every step, tool call, latency point, and failure.
Technology stack
A production stack for agentic AI systems.
The final stack depends on the workflow, systems, data sensitivity, autonomy level, latency, quality targets, and deployment environment. Cosnet selects components that work as one controlled architecture.
Models
Agent orchestration
Knowledge & memory
Tools & integrations
Evaluation & observability
Deployment & security
Selected work
Production environments where intelligent workflows create value.
Cosnet’s agent engineering approach is informed by products handling high-volume interactions, complex operations, connected systems, and real users.
Product perspective
Product foundations for agents that must work beyond the demo.
Agentic systems require more than a model and a chat interface. Cosnet combines workflow design, orchestration, system integration, security, evaluation, and operational controls so the agent can perform useful work without creating unmanaged risk.
Cosnet product thinking
Controlled autonomy by design.
Every agent is designed around a defined job, approved tools, explicit permissions, observable execution, evaluation criteria, and human escalation. The product layer makes the intelligence understandable and usable for the people responsible for the outcome.
Workflow-first agent design
Built around the jobThe design starts with the real business process: who initiates the task, what information is required, which systems are involved, what decisions are allowed, and where exceptions occur. The agent is then shaped around that operating reality rather than forcing the workflow into a generic chatbot.
Tool, memory, and data architecture
Production system thinkingWe define how the agent retrieves knowledge, maintains context, calls APIs, uses business tools, stores state, and separates user-specific or sensitive information. Model selection is treated as one part of the system rather than the entire architecture.
Evaluation and safety engineering
Measurable reliabilityTest suites can measure task completion, factual grounding, citation quality, tool selection, action accuracy, policy compliance, latency, and cost. High-risk actions can require deterministic validation or human approval before anything is changed.
Observability and improvement
Visible operationsExecution traces, tool calls, model outputs, failure reasons, user feedback, and operating cost can be monitored after launch. This provides the evidence needed to improve prompts, policies, retrieval, tools, and workflows without guessing.
Client testimonials
What clients say about working with Cosnet.
Published feedback from organisations that have worked with Cosnet across websites, applications, product delivery, and dedicated teams.
“Cosnet helped us to design a website and working with them was a very positive experience. We would recommend them!”
Chris IssacsCEO, LIONHEART
“Cosnet was able to interpret our needs from the original coding we shared with them and produce a totally flexible solution. The team that was assigned was very professional, we would like to continue with Cosnet as we are very satisfied with their technical expertise and the product they have produced.”
Dennis GoldmenCEO, DREAM
“Cosnet is a reliable partner delivering quality solutions. They excel in IT planning, project management, and clear communication, ensuring outcomes exceed expectations. Highly recommended!”
Matt QuondamCEO, Action Trucks
“Cosnet’s dedicated team is good in managing the app launch and delivery process. We recognize some synergies between our companies and would like to explore options for working together in the future.”
AI SasnowskiCEO, ELBY BIKE
Frequently asked questions
Useful detail before the first conversation.
These answers explain the scope, delivery approach, controls, and practical decisions involved in the service.
A chatbot is generally designed to interpret a message and return a response. An AI agent can work toward a defined goal over several steps: it may retrieve information, select tools, call APIs, maintain task state, make bounded decisions, update connected systems, ask for missing information, and escalate when it reaches a policy or confidence limit. The important distinction is not the label but the level of controlled action. Cosnet defines exactly what the agent may read, decide, change, and submit for approval.
Control is designed at several layers. Tools can be restricted by user, role, workflow, environment, and action. Inputs and outputs can be validated against deterministic rules before execution. Sensitive or high-impact actions can require explicit human approval. The system can also apply policy checks, confidence thresholds, data masking, rate limits, audit logging, exception handling, and safe fallbacks. Automated evaluations and production monitoring are then used to identify regressions or failure patterns.
Yes, provided the systems expose suitable APIs, databases, files, webhooks, or other supported integration methods. The architecture can connect the agent to CRMs, ERPs, ticketing tools, document repositories, email, calendars, payments, messaging platforms, analytics systems, and custom internal applications. During discovery, Cosnet identifies what each integration allows, the permissions required, the quality of the available data, and the operational consequences of allowing the agent to use it.
Testing goes beyond a small set of demonstration prompts. A representative evaluation set can include normal tasks, ambiguous requests, incomplete data, adversarial inputs, policy-sensitive situations, tool failures, permission problems, and escalation cases. Measures may include task completion, factual grounding, citation accuracy, correct tool choice, action accuracy, policy compliance, latency, cost, and recovery behaviour. The evaluation set becomes part of regression testing as prompts, models, knowledge, and integrations evolve.
Yes. A focused prototype is often the right first step when the workflow and value need to be validated. The prototype should still use representative tasks, realistic data boundaries, clear success criteria, and known assumptions so the results are meaningful. After validation, Cosnet can recommend whether to proceed, change the workflow, narrow the agent’s autonomy, improve the data foundation, or stop the initiative before unnecessary production investment.
Tell us the task. We’ll design the agent.
Share the workflow you want to hand off and we will outline a practical architecture and delivery approach.
Book a consultation call →