Custom AI agents
Single-purpose agents designed around one workflow, with instructions, tools and success criteria tailored to how your team currently does the work.
AI Services
AI agent development services with humans in control.
Our AI agent development services build software agents that break down goals, call tools and APIs, and complete multi-step work across your systems. Each agent runs inside defined permissions, logs every decision and pauses for human approval wherever the stakes call for it.
- Tool-using agents
- Approval checkpoints
- Full execution tracing
01 Overview
Agents that finish tasks, not just answer questions
A chatbot responds; an agent acts. Agents read context, decide on a plan, call the tools they have been given, check the results and adjust. That makes them well suited to repetitive knowledge work such as research, reconciliation and case preparation. Our AI agent development services design these loops carefully, because autonomy without boundaries creates risk rather than value.
Reliable agentic AI development comes down to engineering discipline: narrow tool definitions, explicit state, deterministic steps where possible, budgets on time and spend, and evaluation against realistic scenarios. For larger problems we build multi-agent systems where specialized agents coordinate through a supervisor, each with its own instructions, tools and access scope.
02 Capabilities
AI Agent Development capabilities.
End-to-end AI Agent Development: strategy, engineering and the production operations that keep it working.
Multi-agent systems
Coordinated groups of specialized agents, such as researcher, analyst and reviewer, orchestrated through graphs or supervisors so complex tasks stay manageable and debuggable.
Tool and API design
Well-scoped function definitions for databases, SaaS platforms and internal services, including the Model Context Protocol, so agents act precisely and only within allowed boundaries.
Memory and state management
Short-term working memory, long-term retrieval and durable checkpoints that let agents resume interrupted tasks and remember relevant history without bloating context.
Human-in-the-loop controls
Approval steps, editable drafts and escalation paths placed at the points where a mistake would be costly, keeping people accountable for consequential decisions.
Agent observability and evaluation
Tracing of each step, tool call and cost, paired with scenario tests that measure task completion and flag loops, stalls or unsafe actions.
03 How we deliver
From first call to production.
Every AI Agent Development engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Research and report preparation agents
Agents gather information from internal documents and approved web sources, compare findings and draft structured briefs for an analyst to review and finalize.
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UC-02
Finance operations and reconciliation
Match transactions across systems, investigate discrepancies, prepare journal entry suggestions with supporting evidence and queue anything unusual for an accountant's approval.
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UC-03
IT operations and incident response
Autonomous AI agents that read alerts, pull logs, run approved diagnostics and propose remediation steps, escalating to on-call engineers with a clear summary.
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UC-04
Sales and procurement workflow automation
Agents that enrich leads, draft tailored outreach, compare supplier quotes or prepare purchase requests, updating the CRM or ERP as each step completes.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Agent frameworks
- LangGraph
- CrewAI
- AutoGen
- OpenAI Agents SDK
- Model Context Protocol
Reasoning models
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Llama
Observability and runtime
- LangSmith
- Langfuse
- Temporal
- Redis
- Kubernetes
06 Industries
Built for your sector.
We deliver AI Agent Development for regulated and fast-moving industries alike.
What is the difference between an AI agent and a chatbot?
A chatbot mainly responds to messages in a conversation. An AI agent works toward a goal: it plans steps, calls tools and APIs, inspects results and continues until the task is done or it needs help. Agents can run in the background without a chat interface, triggered by events or schedules.
How do you keep autonomous AI agents safe?
Safety comes from limiting what agents can do. We give each agent the minimum tools and permissions required, require human approval for irreversible or high-impact actions, cap time and spend per task, validate tool inputs and log every step. Scenario testing before launch exposes failure modes early.
Which LLM agent frameworks do you use?
We commonly use LangGraph for stateful, graph-based workflows, along with CrewAI, AutoGen and the OpenAI Agents SDK where they fit. For tool connectivity we often use the Model Context Protocol. Framework choice follows the problem; some agents are simpler and more robust as plain code with a model in the loop.
When should we use multi-agent systems instead of a single agent?
Start with one agent whenever possible, since it is easier to test and debug. Multi-agent systems help when a task naturally splits into distinct roles with different tools or permissions, or when a single prompt grows too large to stay reliable. We add agents only when evaluation shows a clear benefit.
How do you measure whether an AI agent is working well?
We build test scenarios based on real tasks and score task completion, correctness of the final output, number of steps, cost and any policy violations. In production, traces and sampled reviews track the same signals, and failed or escalated runs feed back into prompt, tool and workflow improvements.