AI Strategy and AutomationProposed offering

Build AI agents that complete real business tasks, with your systems and rules in control.

We engineer copilots and agents that read from your systems, take approved actions and hand off to a person when judgement is needed, with every step logged and every permission explicit.

Show us the task to automate Try the agent concept

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Agent architecture

User request
Planner
Tool layer
Permission check
Human approval
Audit log

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Excessive CRM administration

A sales representative finishes a call, then spends twenty minutes updating the opportunity, logging notes, creating follow up tasks and drafting a recap email. Multiplied across a team, the CRM is accurate on Friday and stale by Monday.

Why it happens: The CRM is designed as a database to be filled in, and the people with the context are also the people with the least time.
What it costs: Forecasts rely on incomplete data and representatives lose selling time to data entry.
How we approach it: A sales copilot reads call notes or transcripts, proposes field updates and follow up tasks with the evidence beside each, and writes them to the CRM after the representative approves. Low risk fields can be configured to update automatically.
What to measure: Minutes of admin per opportunity, field completeness, and forecast accuracy over time.

Support teams switching between tools

An agent answers a billing question by moving between the helpdesk, the billing system, the order system and a knowledge base. Each ticket takes several tab changes and copy and paste, and details get lost between them.

Why it happens: Systems were bought at different times and never joined, so the person becomes the integration layer.
What it costs: Longer handling time, inconsistent answers and agent fatigue.
How we approach it: A support agent assembles the customer context from each system, drafts a reply with cited policy, and prepares the action such as a refund. The human agent reviews and sends, or the system sends automatically for low risk categories.
What to measure: Average handling time, first contact resolution, and the share of tickets resolved without tool switching.

Manual coordination of approvals

A purchase request needs three approvers. The requester chases by message, an approver is on leave and nobody knows who the delegate is, and the request sits for a week.

Why it happens: The approval rules live in people's heads and in email threads, not in a system that can route and remind.
What it costs: Delays, shadow processes and audit gaps.
How we approach it: An operations agent applies the approval matrix, finds the right approver or delegate, assembles the supporting context, sends reminders and records the decision, escalating on timeout.
What to measure: Approval cycle time, number of reminders sent by humans, and audit exceptions.

Chatbots unable to execute business actions

A customer asks a chatbot to change a delivery address. The bot explains how to do it on the website, and the customer calls the contact centre.

Why it happens: The bot was built as a question answering layer and has no secure access to the systems that perform the action.
What it costs: Deflection numbers look good, but customers who needed an action are more frustrated than before.
How we approach it: We give the agent narrowly scoped tools, such as update address within a window, with authentication, validation, limits and confirmation steps. High impact actions require human approval.
What to measure: Task completion rate, escalation rate, and customer satisfaction on completed tasks.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Sales copilots

Meeting briefs, call summaries, proposed CRM updates and follow up drafts, grounded in your CRM and notes.

Customer need: Cut CRM admin and prepare for conversations.
Integration: Salesforce, HubSpot, Dynamics or a custom CRM through APIs, plus calendar, email and call recording sources.
Deliverable: Copilot embedded in the CRM or a side panel, with approval workflow.
Business value: More selling time and cleaner pipeline data.

Support agents

Customer context assembly, cited answer drafting, action preparation and automatic handling of defined low risk categories.

Customer need: Resolve tickets with less swivel chair work.
Integration: Helpdesk, order, billing and knowledge systems.
Deliverable: Agent assist panel and optional autonomous lane with rules.
Business value: Shorter handling time and consistent answers.

Operations agents

Agents that follow a documented procedure across systems such as onboarding checks, reconciliations and status chasing.

Customer need: Run routine multi step operational tasks.
Integration: ERP, ticketing, email and document stores.
Deliverable: Agent with a runbook, tool set and exception queue.
Business value: Less manual coordination and a clear audit trail.

Tool integration layer

Typed tool definitions with validation, rate limits, idempotency and per tool permissions. Support for MCP style tool interfaces where they fit.

Customer need: Give agents safe, narrow access to systems.
Integration: Your internal APIs, queues and databases.
Deliverable: Tool catalogue with tests and documentation.
Business value: Actions that are predictable and reversible.

Permission controls

Run actions with the requesting user's identity or a scoped service identity, enforce role rules and log the authority used.

Customer need: The agent must never exceed the user.
Integration: SSO, role directories and policy engines.
Deliverable: Permission model and policy tests.
Business value: Safe delegation and defensible audits.

Human approval workflows

Risk based approval: auto, confirm, or require manager approval, with the evidence and proposed change displayed.

Customer need: Keep people in control of consequential actions.
Integration: Chat, email and in app approval surfaces.
Deliverable: Approval UI, thresholds and reporting.
Business value: Automation without loss of control.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Choose the task

Select a narrow, high volume task with clear rules and measurable outcomes. We document the procedure as a human would follow it.

Define tools and permissions

List the systems, the actions allowed, the identity used and the limits. Anything irreversible is marked for approval.

Select models and design prompts

Compare candidate models on your task examples for accuracy, latency and cost. Design instructions and tool schemas, and keep them in version control.

Build the agent and tools

Implement the orchestration, tool layer, memory policy and approval flows with deterministic checks around model decisions.

Evaluate on real cases

Create a labelled set of past cases, measure task completion, tool call correctness and unsafe action attempts, and test prompt injection and permission boundary cases.

Pilot with human in the loop

Run with approvals on for every action, measure the approval rate and edits, then relax approvals only where the data supports it.

Deploy with observability

Release behind feature flags with tracing, cost metering, alerting and a rollback path.

Improve continuously

Review failures weekly, add them to the regression set, tune prompts or tools, and re-evaluate before each release.

Solution in action: A support agent that prepares a refund and waits for approval

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

Customers email asking for refunds on late deliveries. Handling takes several system lookups and a manager approves anything above a threshold.

Existing workflow

The agent reads the email, opens the order system, checks the courier status, reads the refund policy, calculates the amount, enters the refund and notes the ticket.

Improved workflow

The support agent reads the email, retrieves the order and courier status through typed tools, finds the applicable policy clause, calculates the amount, drafts the reply and prepares the refund. Below the threshold it executes after the human agent confirms. Above it, it routes to a manager with the evidence attached.

A customer emails a refund request.

Systems involved

Helpdesk, order management, courier tracking API, payment system, policy knowledge base.

Data movement

Ticket text, order record, courier events, policy passage with citation.

Human decisions

Agent confirms the draft. Manager approves refunds above the limit.

Automation opportunities

Context assembly, calculation, drafting and ticket note writing.

Exception handling

Missing order, ambiguous identity or conflicting courier data produce a clarification request or hand off to a person, never a guess.

Resulting user experience

The customer receives a correct answer faster, and the human agent reviews a prepared case instead of assembling one.

KPIs to evaluate

Task completion rate
Escalation rate
Handling time
Cost per resolved ticket

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

Agent or copilot deployed to a staging and production environment
Tool catalogue with schemas, tests and documentation
Permission model and policy tests
Evaluation suite with labelled cases and scoring
Approval workflow and exception queue
Observability dashboard with traces, cost and outcomes
Runbook and incident procedure
Training for the people who supervise the agent

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Models (proposed implementation options)

OpenAI compatible APIs
Anthropic
Azure AI
AWS Bedrock
Google Cloud AI

Orchestration

Tool calling frameworks
Workflow engines
Event driven services
MCP style tool interfaces

Integration targets

Salesforce
HubSpot
Zendesk
ServiceNow
Custom REST and GraphQL APIs

Evaluation and tracing

LangSmith
Arize
Galileo
Open source evaluation frameworks

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Solution ConceptInteractive demo with simulated data

Operations agent with approval for claim status and refunds

The insurance and hospitality demonstrations show agents taking prepared actions with human approval, using simulated systems and synthetic data.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Task completion

Share of tasks finished correctly without human rework.

Escalation rate

Share handed to a person, with reasons.

Processing time

Elapsed time per task against baseline.

Cost per action

Model, infrastructure and review cost per completed task.

Unsafe action attempts

Blocked attempts per thousand tasks.

Questions buyers ask

What is the difference between a copilot and an agent?

A copilot assists a person who stays in control of each step. An agent can plan and perform several steps and use tools. Many solutions combine both, with approvals that loosen as trust is earned from data.

How do you stop an agent doing something harmful?

Through layers: narrow tools with validation, permissions tied to the user, limits on amounts and frequency, human approval for consequential actions, and evaluations that include adversarial cases such as prompt injection.

How long does a first agent take?

A single task agent with two or three integrations is typically scoped as a pilot of several weeks. Integration availability and the quality of the documented procedure are the main drivers.

Which model will you use?

We pick based on measured results on your cases, latency, cost and data policy. The design keeps models swappable so you are not locked in.

Who owns the code and prompts?

Ownership terms are set in the contract. Our standard approach is that you own the deliverables built for you, including tool definitions, prompts and evaluation sets, and that we document them.

How is it supported after launch?

Through monitoring, weekly failure review and a regression suite that runs before each change. Support terms can be arranged under the Software Maintenance and Product Support service.

Related services

Industries where this applies

Show us the workflow you want an agent to handle.

Describe the task, the systems and the rules. We will tell you what can be automated, what needs approval and how to measure it.

Discuss an agent workflow Explore a demonstration