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Build AI agents around a defined workflow.
Connect AI reasoning to controlled actions in your business systems. Begin with a bounded task, clear permissions and a review process that lets your team supervise important actions.
★★★★★5.0· 57 client reviews
Overview
AI Agent Development, explained
An AI agent is software that reads a request, decides which steps to take and uses approved tools to complete them, such as looking up a customer record, checking stock or drafting a refund. Unlike a chatbot that only answers, an agent acts, so the important engineering work is deciding what it is allowed to do and how a person stays in control.
We build agents around one well-defined workflow at a time. Each agent gets a narrow set of tools, explicit permissions, logs of every action and an escalation path when it is unsure. That makes behaviour reviewable, keeps costs predictable and lets your team expand responsibility gradually as trust is earned.
Before you build
Decisions worth settling first
Settling these points early keeps scope realistic and makes the estimate meaningful.
01
The job the agent owns
Describe the start and end of the task, the inputs it receives and what a finished result looks like.
02
Tools and permissions
List the systems the agent may read and the actions it may take. Write actions usually start behind approval.
03
Approval and escalation
Decide which actions need sign-off and how the agent hands a case to a person with full context.
04
Evaluation scenarios
Collect real examples, including edge cases, to test the agent before and after each change.
05
Failure handling
Define what happens when a tool times out, data is missing or the agent cannot decide.
06
Cost per task
Estimate model calls per task and set limits so a runaway loop cannot create an unexpected bill.
Services
What our AI agent development services include
Agent discovery and design
Map the workflow, tools, permissions and review points, then define a pilot with measurable acceptance criteria.
Single-task agents
Agents that complete one job end to end, such as triaging tickets or preparing order updates.
Multi-agent workflows
Coordinated agents for research, drafting and checking where one agent’s output feeds another.
Tool and API integration
Secure connectors to CRMs, ERPs, helpdesks, databases and internal APIs, including MCP-style tool servers.
Agent copilots
Assistants inside your app that suggest next steps while the user stays in charge of every action.
Monitoring and evaluation
Action logs, quality dashboards, regression tests and alerts that make behaviour visible in production.
How it is built
The layers behind a dependable result
Every layer has an owner, tests and documentation, so the system can be supported after launch.
01
Instructions and policy
The role, rules and boundaries the agent must follow, written so they can be reviewed and versioned.
02
Tools
Typed functions the agent can call, each with input validation, permission checks and rate limits.
03
Memory and context
Retrieval of the records and documents needed for the current task, scoped to what the user may access.
04
Oversight
Approval queues, audit logs, evaluation runs and alerts that keep people informed and in control.
Choosing an approach
Which approach fits your problem?
| Type | Role | Best for |
|---|---|---|
| Scripted automation | Runs fixed steps | Predictable tasks with no judgement |
| Chatbot | Answers questions | FAQs and guided conversations |
| Copilot | Suggests, user acts | Expert users who want speed with full control |
| AI agent | Plans and acts within permissions | Bounded multi-step tasks with review |
| Multi-agent system | Several agents coordinate | Complex work split into specialised steps |
Use cases
Where it helps
Customer support
Gather order and account context, propose a resolution and prepare the action for an agent to approve.
Sales operations
Research leads, update CRM fields and draft follow-up emails for review.
Finance back office
Match invoices to purchase orders, flag differences and prepare approval requests.
IT and internal help
Answer policy questions, reset routine settings and open tickets with the right details.
Operations
Monitor queues, chase missing information and summarise daily exceptions for supervisors.
Research and reporting
Collect data from approved sources and assemble structured briefings with citations.
Illustrative use case
A support agent can collect the relevant context and draft an action for approval before an operator changes a customer record.
Why PoliteTech
Why teams choose PoliteTech
Scope before commitment
Priorities, assumptions and acceptance criteria are agreed in writing before a delivery plan is fixed.
Engineers who build
Recommendations come from the people who design, code and support the software, so they reflect real implementation effort.
Visible progress
A shared backlog, regular demos and recorded decisions keep you informed without chasing updates.
Practical AI
Evaluation sets, review steps, cost limits and monitoring are part of every AI feature we ship.
Flexible engagement
Start with a scoped project, a dedicated team or support, and change the model as your roadmap evolves.
Clear ownership
Repositories and accounts can live in your organisation, with IP and confidentiality terms agreed in the contract.
Industries
Where this work fits
Retail and Ecommerce
Build web and mobile journeys around product discovery, ordering and customer service.
Financial Software
Support reporting, document processing and internal operations through defined application workflows.
Logistics Software
Connect planning, task assignment and operational updates in one workflow.
Education and Elearning
Create platforms for content, activities and progress tracking.
Fitness and Wellness Apps
Support tracking, coaching content and user engagement around a clear product goal.
Manufacturing Software
Replace fragmented tracking with workflows for quotations, work status and supporting documents.
Real Estate Applications
Create listing, enquiry and internal coordination tools around your business process.
Business Operations
Build task, approval and reporting tools around the way your team works.
Process
A practical path from requirements to release.
Each stage ends with something you can review: a scope, a prototype, a working increment or a release.
01
Understand
Discuss your users, business needs, existing systems and delivery constraints.
02
Define
Agree scope, technical approach, responsibilities and a delivery plan.
03
Design and build
Develop in manageable increments with visible progress and review points.
04
Test and release
Validate agreed workflows, resolve release blockers and prepare deployment.
05
Support and improve
Document the handover, review production feedback and plan the next priorities.
Technology
Tools and platforms we work with
OpenAIGoogle GeminiLlamaMistralLangGraphLangChainModel Context ProtocolPythonNode.jsFastAPIPostgreSQLRedispgvectorAWSAzureGoogle Cloud
Cost
What shapes the cost of your project?
There is no useful price without scope. These are the factors we review when preparing an estimate, and the estimate lists every assumption so you can see what would change it.
01
Number of workflows
Each workflow needs its own prompts, tools, evaluation set and review design.
02
Data preparation
Cleaning, labelling, chunking and access rules often take more effort than the model itself.
03
Integrations
Connections to CRMs, ERPs, document stores and identity providers add design and testing work.
04
Evaluation and review
Building test sets, review queues and monitoring is essential for reliable results.
05
Model and hosting
Hosted APIs, open models and private deployment have very different setup and running costs.
06
Security and compliance
Sensitive data, audit needs and data residency add controls and documentation.
Engagement models
Choose the engagement that fits your next step.
Dedicated Team
For a changing roadmap or ongoing development. Agree roles and working process, then manage priorities through a shared backlog.
Best for team extension and long-term product work.
Scoped Project
For a defined product or milestone. Align on requirements, deliverables and acceptance criteria, with changes reviewed before they affect the plan.
Best for MVPs, integrations and focused releases.
Maintenance and Improvement
For software that already exists. Start with a technical review, then agree support routines and planned improvements.
Best for live applications and modernisation.
FAQ
Questions about AI agent development
How is an AI agent different from a chatbot?
A chatbot responds with text. An agent can also take actions in other systems through approved tools, which is why permissions, logging and approval steps matter more.
How do you stop an agent from taking a wrong action?
Agents only get the tools they need, write actions can require approval, inputs are validated and every step is logged. Uncertain cases are escalated rather than guessed.
Can the agent work with our existing systems?
Usually, yes. We connect through APIs, databases or integration platforms and review authentication and data access with your team first.
Which frameworks and models do you use?
We choose per project, commonly using OpenAI or Gemini models, open models where data must stay private, and frameworks such as LangGraph or custom orchestration when simpler is better.
When do we need multiple agents?
Only when a task naturally splits into steps with different skills or checks. Many workflows are more reliable with a single agent and clear tools.
How do you measure whether the agent works?
We agree success measures up front, such as task completion, escalation rate, accuracy on test cases and time saved, then track them during the pilot.
How do you control running costs?
Through model selection, caching, limits on steps per task and monitoring of cost per completed task.
What does a proof of concept show?
That the agent can complete the chosen task on real examples within agreed quality and cost limits, which gives you evidence for a production decision.
Can we begin with a focused scope?
Yes. We can review your current situation and agree a bounded first engagement before a wider build.
How are pricing and timelines determined?
We review requirements, technical complexity, team needs and delivery constraints before proposing an engagement. Estimates list assumptions and what may change the plan.
Can we discuss a project under an NDA?
Yes. Let us know before sharing confidential materials so we can agree confidentiality terms first.
Discuss your AI agent development needs
Share your goals, current situation and the help you need. We will review your message and suggest a practical next step.