CodeBase Coders designs and builds custom AI agents that understand goals, use your tools and data, and complete multi-step work across your systems, with the permissions, approvals and monitoring that make agentic AI safe to run in a real business.
A chatbot answers questions. An AI agent gets work done: it breaks a goal into steps, decides which tools to use, checks the results and keeps going until the task is complete, or asks a person when it should. AI agent development is about giving that autonomy the right boundaries, and that is where our engineering focus sits.
From identifying the right first agent to running a fleet of agents in production, our AI agent development services cover each stage, delivered by one team of AI, backend and integration engineers.
Agent use-case discovery
Autonomy level and scope definition
Agent workflow design
Success and safety metrics
We find the workflows where an agent will save real time, define exactly what it should and should not do, choose the level of autonomy and design the success metrics, before anything is built.
Task-specific AI agents
Tool and function definitions
Memory and context management
Agent dashboard and review UI
Our custom AI agent development builds agents around your processes, data and tools: the prompts and reasoning loop, tool definitions, memory, error handling and the interface people use to assign work, review results and approve actions.
Orchestrator and worker agents
Agent-to-agent handoffs
Reviewer and verifier agents
Parallel and conditional workflows
For larger processes we build multi-agent systems in which specialised agents, such as a researcher, a planner, a checker and an executor, collaborate under an orchestrator, so complex work is split into reliable, testable parts.
Event-triggered agents
Scheduled background agents
Alerts and escalation rules
Spending and action limits
Autonomous AI agent development for jobs that should run without prompting: monitoring inboxes, queues or data feeds, reacting to events, and completing routine tasks on a schedule, with limits, alerts and escalation rules keeping them in check.
Chat agents that take action
AI voice agents
WhatsApp and messaging agents
Human handoff with context
Agents that talk to customers or employees by chat or voice and then act on the request: checking an order, rebooking an appointment, updating an account or opening a ticket, rather than simply answering questions.
MCP server development
CRM, ERP and SaaS connectors
Role-based tool permissions
Sandboxed execution
We give agents safe access to your systems by building API connectors and Model Context Protocol (MCP) servers, mapping permissions to real user roles, and adding sandboxes for actions that should be tested before they touch production data.
Task-based evaluation suites
Prompt-injection red-teaming
Model and prompt comparisons
Regression testing on changes
Agents are tested like software and like people. We build evaluation suites of realistic tasks, measure success rate, accuracy, cost and time per task, red-team for prompt injection and misuse, and re-run the evaluation on every change.
LLM cost reduction
Latency optimisation
Model routing by task
Retrieval and memory tuning
We make agents faster and cheaper without losing quality: smaller models for simple steps, caching, better retrieval, fewer unnecessary tool calls and tighter prompts, all measured against your evaluation suite.
Tracing and observability
Failure alerts and recovery
Model and tool updates
Permission and behaviour audits
Once agents are live we run them properly: tracing and dashboards, failure alerts, feedback review, model and tool updates, and periodic audits of what agents are doing and whether their permissions are still right.
Bring us a process your team finds repetitive. We will tell you whether an AI agent can handle it, how much autonomy is sensible and what a first version would look like.
Most business agents fall into a few practical categories. Many projects start with one and grow into several agents working together.
Take a defined job, such as processing an order, onboarding a customer or reconciling records, from start to finish across several systems.
Gather information from documents, databases and the web, compare sources and produce summaries, reports or recommendations with citations.
Resolve customer requests end to end, including refunds, rebookings and account changes, within the rules you set, escalating edge cases.
Research prospects, qualify inbound leads, draft personalised outreach and keep the CRM updated automatically.
Read invoices, contracts, claims and forms, validate them against rules and systems, and route exceptions to the right person.
Triage alerts, investigate incidents, run approved runbooks and draft fixes or change requests for engineers to review.
Watch data, prices, inventory or compliance signals continuously and act or alert when something needs attention.
Teams of specialised agents that divide complex work such as research, drafting, checking and executing, coordinated by an orchestrator.
Under the hood, agents differ in how they decide what to do next. We choose the architecture that gives the reliability your use case needs, often combining several.
Respond directly to inputs with defined actions; fast and predictable for well-understood tasks.
Plan a sequence of steps toward a stated goal and adjust when a step fails.
Weigh options against costs and priorities to pick the best action, not just any valid one.
Improve from feedback and outcomes over time, within limits you control.
Combine deterministic rules for critical steps with LLM reasoning where flexibility helps.
Several specialised agents coordinated by an orchestrator for complex, multi-stage work.
Agentic AI is not limited to one team. These are the departments where AI agents most often pay off first.
Resolve tickets end to end, process returns and refunds within policy, and summarise cases for agents.
Enrich and qualify leads, prepare account research before calls and keep pipeline data current.
Draft campaign content, monitor competitors, analyse performance and prepare reports.
Match invoices, chase missing documents, reconcile accounts and flag anomalies for review.
Screen applications against criteria, schedule interviews, answer policy questions and run onboarding steps.
Triage alerts, reset access, run runbooks and prepare incident summaries.
Track orders and shipments, update inventory, and coordinate suppliers when delays occur.
Review documents against checklists, track obligations and prepare evidence for audits.
Demos of AI agents are easy; agents that behave reliably with real data, real permissions and real customers are not. We build agents the way we build any production software, with clear boundaries, tests, monitoring and ownership.
We design AI agents around the systems, regulations and workflows of your industry. Explore the industries we build software for:
We build on proven agent frameworks and cloud agent platforms, the leading foundation models, and the memory, orchestration and observability tools agents need in production.
LangChain
OpenAI Agents SDK
Claude Agent SDK
Amazon Bedrock Agents
Azure AI Foundry Agent Service
GPT, Claude & Gemini models
Llama, Mistral & Qwen
Pinecone / Qdrant / pgvector
Mem0 / Redis memory
n8n automation
Playwright browser tools
Python / FastAPI
TypeScript / Node.js
Docker / Kubernetes
AWS / Azure / GCP
Our AI agent development process starts narrow and earns autonomy step by step, so each agent proves itself before it is trusted with more.
We agree the task, success metrics, allowed actions, approval points and what the agent must never do.
We choose the model, framework, memory, tools and single- or multi-agent structure, and design the review interface.
We develop prompts, reasoning loops, retrieval and tool definitions, and build an evaluation set of realistic tasks.
We connect the agent to your APIs and data through scoped credentials, sandboxes and audit logging.
We run evaluations and adversarial tests, then pilot with human approval on every action before relaxing controls.
We trace performance in production, improve from feedback and extend the agent's scope as it earns trust.
Describe the work you want an AI agent to handle and the systems involved. We will reply with a suggested design, the right level of autonomy and next steps.
What the agent should do, the tools it needs and where people must stay in control.
Architecture, scope, safety controls, timeline and cost estimate.
Start with a supervised pilot and expand autonomy as results prove out.
AI agent development is the design and engineering of AI systems that can pursue a goal on their own: plan the steps, use tools such as APIs, databases and applications, check results and complete multi-step tasks. It covers choosing the model and framework, building tools and memory, integrating with business systems, setting guardrails and approvals, and testing and monitoring the agent in production.
A chatbot mainly answers questions in a conversation. An AI agent takes actions to achieve a goal, such as updating records, sending emails, processing a refund or running a workflow across several systems, often without a conversation at all. Many modern chatbots include agent capabilities; see our AI chatbot development services for conversational use cases.
Creating a business-ready AI agent involves six steps: define the task and its boundaries; choose a model and agent framework; give the agent tools (APIs, data access) with scoped permissions; add memory and retrieval so it has the right context; build an evaluation set and test, including for prompt injection; then pilot with human approval and monitor it in production. The tools part is usually where most of the engineering effort goes.
AI agent development cost depends on how many systems the agent must use, how complex and variable the task is, the level of autonomy and approvals required, security and compliance needs, and expected volume, which drives model and hosting costs. A single-task agent with a few integrations costs far less than a multi-agent system across many platforms. We provide a written estimate after a short discovery session.
A focused agent pilot can often be built in a few weeks. Production agents with several integrations, approval workflows and thorough evaluation typically take a few months, and multi-agent systems longer. API access, data readiness and how much autonomy you want to grant are the main drivers.
Yes, when they are scoped well. Agents work best on tasks with clear goals, reliable tools and a way to check results. They struggle when the task is vague, tools are unreliable or there is no evaluation. That is why we start with a narrow, measurable task, test against real examples and expand autonomy only as the agent proves itself.
We follow core principles of secure AI agent development: least-privilege tool access tied to real user roles, sandboxes for risky actions, spending and rate limits, human approval for high-impact actions, input filtering and defences against prompt injection, and full tracing of every decision and tool call so behaviour can be audited and corrected.
We work with LangGraph, LangChain, CrewAI, AutoGen, the OpenAI Agents SDK, Claude Agent SDK and Google's Agent Development Kit, cloud services such as Amazon Bedrock Agents and Azure AI Foundry Agent Service, and custom orchestration where it fits. For models we use GPT, Claude, Gemini and open-weight options like Llama and Mistral, chosen per task on accuracy, cost and data-residency needs.
Yes. Agents are only as useful as the systems they can act on, so integration is central to our work. We connect agents to CRMs, ERPs, helpdesks, email, documents and custom applications through APIs and MCP servers. Learn more about our AI integration services.
Look for a partner that can show how it limits agent permissions, tests agents before launch, handles failures and keeps humans in control, not just impressive demos. It should integrate with your real systems, be open about costs, be independent of a single model vendor and hand over the code, prompts and evaluation sets. If you are still deciding whether agents fit, start with AI consulting.
CodeBase Coders provides end-to-end AI agent development services: agent strategy and design, custom and multi-agent systems, autonomous and voice agents, tool and MCP integration, evaluation, optimisation and ongoing AgentOps. For broader AI builds see our AI development services. Contact us to scope your first agent.
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