Agentic AI development
AI agents that get the task done, not just talk about it.
Agleam Labs builds AI agents that read a request, plan the steps, and carry them out through your systems: looking up records, updating tickets, sending messages, and handing off to a person when needed. You decide how much autonomy each agent has and where approval is required.
- Agents that act through your APIs
- Supervised or autonomous modes
- Multi-agent workflows
- Full logs of every action
What agentic AI means
From answering questions to completing work.
A chatbot replies. An agent acts. Agentic AI combines a language model with tools, such as your CRM, ticketing system, database, or calendar, so it can take a goal, break it into steps, call the right services, check the results, and finish the job. Where generative AI produces content and RAG grounds answers in documents, agents are about taking action.
Giving software that kind of freedom needs careful engineering. We define exactly which tools each agent may use, what it may change, and when it must stop and ask. Every decision and action is logged, so you can review what happened, tighten the rules, and extend autonomy only when the agent has earned it.
What we build
Agents designed for real operations.
From a single helper inside one workflow to a team of coordinated agents, each build has defined boundaries and clear ownership.
01 Task-specific agents +
- Single purpose
- Scoped permissions
- Tool calling
- Fast to deploy
Agents focused on one job, such as qualifying leads, processing refund requests, or preparing reports, with the tools and permissions that job requires and nothing more.
02 Multi-agent workflows +
- Orchestration
- Planner and executor
- Reviewer agents
- Hand-offs
Several specialised agents that plan, research, execute, and check each other's work, coordinated by an orchestrator for longer and more complex processes.
03 Tool and API integration +
- REST APIs
- MCP
- Function calling
- Webhooks
- Databases
Secure connections that let agents read and write in your business systems, through existing APIs, custom endpoints, or Model Context Protocol servers.
04 Human-in-the-loop controls +
- Approvals
- Escalation
- Confidence checks
- Override
Approval steps for sensitive actions, confidence thresholds, and escalation to the right person, so agents speed work up without taking risky decisions alone.
05 Customer-facing agents +
- Chat
- Voice
- Order lookup
- Self-service
Support and service agents that resolve requests end to end, such as changing a booking or checking an order, rather than only pointing customers to a help page.
06 Back-office automation agents +
- Queues
- Data entry
- Reconciliation
- Scheduling
Agents that handle data entry, reconciliation, document routing, and follow-ups across internal tools, working through queues around the clock.
07 Agent observability and safety +
- Tracing
- Rate limits
- Sandboxing
- Evaluation
- Audit trail
Tracing of every step an agent takes, cost and rate limits, sandboxed execution, and test suites that catch regressions before changes go live.
Why agents
More work finished, with control kept where it matters.
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Requests completed, not queued
Agents act on routine requests straight away instead of waiting for someone to pick them up.
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Work around the clock
Queues keep moving overnight and at weekends without extra shifts.
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People on the hard cases
Staff handle exceptions and judgement calls while agents take the repetitive steps.
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Autonomy you set
Start supervised, review the logs, and widen what agents can do as confidence grows.
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Every action traceable
Complete records of what each agent did and why, ready for review or audit.
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Wondering which tasks an agent could take over? Book a free consultation and we will assess it with you.
Book a free consultation →
Why Agleam Labs
Agents built with engineering rigour.
Hover or tap a card to see what it means in practice.
How we work
From a manual task to a trusted agent.
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01
Select the task
We identify a task with clear rules, good volume, and measurable outcomes, and map every step and exception.
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02
Define tools and limits
We decide which systems the agent can use, what it may change, and where a person must approve.
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03
Build and test
We build the agent and its integrations, then test it against real historical cases in a safe environment.
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04
Supervised launch
The agent goes live with review in place; autonomy expands as results are proven in the logs.
Agleam Labs in numbers
- Enterprise apps delivered
- 50+
- Enterprise apps delivered
- On-time delivery
- 99.8%
- On-time delivery
- Years of senior experience
- 10–30
- Years of senior experience
- Years of DevOps experience
- 20+
- Years of DevOps experience
- Technologies in production
- 20+
- Technologies in production
- Office, worldwide clients
- 1
- Office, worldwide clients
Technology
Tools for building dependable agents.
We are framework-agnostic and choose what suits your use case, models, and hosting requirements.
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Python
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TypeScript
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Model Context Protocol
- n8n
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Node.js
- OpenAI
- Claude
- Gemini
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Serverless
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Docker
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AWS
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Azure
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Monitoring
Industries
Agents for operations-heavy sectors.
Agents add the most value where teams handle large volumes of repeatable requests across several systems.
E-Commerce
Commerce technology connecting customers, inventory, payments, and retail operations.
FinTech
Payment, lending, and transaction platforms for always-on financial operations.
Logistics
Platforms for fleets, shipments, warehouses, and connected supply-chain operations.
Telecom
Digital platforms, integrations, and service systems for continuous delivery.
Real estate management
Property platforms for portfolios, leasing, tenants, sales, and day-to-day operations.
Technology and SaaS
SaaS platforms and AI products built to scale with users, features, and integrations.
FAQs
Agentic AI questions.
Straight answers about agentic ai with Agleam Labs.
Talk to an expert
Tell us what you are working on. A senior engineer will reply with honest advice and a suggested next step.
Book a call →What is the difference between an AI agent and a chatbot? + −
A chatbot answers questions. An agent can also take action: it plans steps, calls your systems through APIs, checks the results, and completes a task such as updating a record or processing a request.
How do you stop an agent doing something it should not? + −
Permissions are enforced in code, not left to the model. Agents can only call the tools we give them, sensitive actions require approval, and spending, rate, and scope limits stop runaway behaviour.
Should our agents be autonomous or supervised? + −
Most start supervised, with a person approving key actions. Once the logs show the agent is reliable on a type of task, you can let it complete that task on its own.
Which systems can agents work with? + −
Anything with an API, database access, or a structured interface: CRMs, helpdesks, ERPs, calendars, email, and internal tools. If an API is missing, we can build a safe one.
When do we need multiple agents rather than one? + −
When a process has distinct stages that benefit from different instructions or tools, such as research, drafting, and checking. For a single well-defined task, one agent is simpler and cheaper.
How long does it take to build an agent? + −
A focused single-task agent typically takes four to eight weeks including integration and testing. Multi-agent workflows across several systems take longer and are delivered in stages.
Start a project
Let's build your first agent.
- A free consultation with a senior engineer
- Clear scope, timeline, and budget
- Confidential, with an NDA on request
- Support after delivery
Prefer email?
info@agleamlabs.comHand the routine work to an agent.
Describe the task your team repeats most. We will tell you whether an agent can handle it and how we would build it safely.