Our custom AI agent development covers agents that carry out multi-step work across your tools and data. Risky actions need a person’s approval, and every step the agent takes is logged.
An AI agent reads data, decides what to do next and acts through your tools and APIs. We build agents that do this inside clear limits: scoped permissions, human approval where the stakes are high, and an audit trail of every step.
AI agent development is building software that uses large language models (LLMs) to plan, reason and act across several steps to complete a task. A chatbot answers one message at a time. An agent reads data, calls APIs, makes decisions and carries out actions in sequence.
For example, an agent can receive a sales lead by email, research the company, enrich the CRM record, draft a personalised reply, schedule a follow-up and tell your sales team, with a person approving the email before it goes out.
| Feature | Chatbot | AI copilot | AI agent |
|---|---|---|---|
| Interaction | Answers single questions | Helps a person in real time | Works through tasks across systems |
| Memory | Within one session | Aware of the current context | Keeps state across tasks and sessions |
| Tool use | None or basic | Suggests actions | Carries out actions through APIs and tools |
| Decisions | Rule-based | Recommends options | Plans and decides within set limits |
| Best for | FAQs, simple support | Coding, writing, in-app help | Multi-step workflow automation |
From single-purpose agents to several agents coordinated across departments.
Single-purpose agents for repetitive processes: invoice processing, data entry, report generation, email triage and scheduling.
They connect to Google Workspace, Microsoft 365, Slack and your internal systems through APIs and remove copy-paste work.
Customer-facing agents that go beyond FAQ bots. They keep context, look up your knowledge base through retrieval (RAG) and handle multi-turn conversations.
They hand over to a person when needed and can complete tasks such as refunds, account updates or bookings within the limits you set.
Several specialised agents working together: one collects data, another analyses it, a third writes the report and a fourth sends it out.
An orchestrator handles hand-offs, errors and ordering, which suits cross-department workflows.
Agents that gather information from the web, databases, documents and APIs and turn it into structured output.
Typical outputs: market research, competitor analysis, regulatory monitoring summaries and literature reviews, with sources cited.
Agents your staff use to query internal documents, policies, SOPs and databases in plain language.
Built on retrieval-augmented generation, so answers are grounded in your own documents and cite them.
Each phase has a named owner, documented changes and something you can review before the next one starts.
We map the workflow, find the steps worth automating and define the agent's scope. You get workflow diagrams, a feasibility note and a ranked list of use cases.
Model choice, tool integrations, memory, error handling, permissions and the points where a person must approve. Documented before we build.
Two-week sprints: agent logic, connections to your CRM, ERP, databases and SaaS tools, retrieval where needed, and logging. A working demo at the end of each sprint.
Functional, load and edge-case tests, prompt-injection and jailbreak checks, hallucination checks and adversarial testing. Approval flows for higher-risk actions.
Production release with action logs, audit trails, dashboards and alerts, plus documentation and training for your operations team.
We monitor behaviour, tune prompts and policies, extend capabilities and adapt to changes in your workflow or in the underlying models.
Agents earn their keep in repetitive, multi-step, knowledge-heavy work. These are typical places to start.
We're model-agnostic and choose components for accuracy, latency, running cost and data-privacy needs. Self-hosted options are available where rules require it.
| Layer | Technologies |
|---|---|
| LLM providers | OpenAI, Anthropic Claude, Google Gemini, and open models such as Llama and Mistral |
| Agent frameworks | LangChain, LangGraph, CrewAI, AutoGen and custom orchestration |
| Vector databases | Pinecone, Weaviate, Qdrant, ChromaDB |
| Data pipelines | Apache Kafka, Airflow, custom ETL and ELT |
| Integration | REST APIs, GraphQL, webhooks, Zapier, Make and custom connectors |
| Cloud and infrastructure | AWS, Google Cloud, Azure, and on-premise where required |
| Monitoring and observability | LangSmith, Langfuse, custom dashboards and logging |
| Security and governance | Encryption at rest and in transit, role-based access, audit trails, controls mapped to SOC 2 and ISO 27001 |
Every engagement starts with a free 30-minute discovery call about your workflows and constraints. You get a written proposal with pricing within two working days.
One agent, one workflow, to prove it works and measure the effect. Built in 4–8 weeks as a PoC or MVP.
A full agent with integrations, monitoring and guardrails, scoped as a project after a 1–3 week discovery.
Three to fifteen or more engineers and AI specialists working only on your agents and AI roadmap. Ready in 2–4 weeks, on 30 days' notice.
Monitoring, tuning and new capabilities for agents already in production.
Software that uses an LLM to understand an instruction, plan a sequence of actions, call tools and APIs, and carry out the task. The LLM does the reasoning; explicit tool logic and guardrails decide what it's allowed to do.
A pilot agent is a 4–8 week build. A production agent with full integrations is scoped as a project after a 1–3 week discovery, and its schedule depends on how many systems it touches and how much testing the risk calls for.
A chatbot answers messages within a conversation. An agent works across systems: reading email, updating databases, calling APIs and finishing multi-step tasks without a person driving every step.
Yes, with anything that has an API, including Salesforce, HubSpot, SAP, Google Workspace, Microsoft 365, Slack, custom ERPs and databases. For older systems without APIs we look at custom connectors or carefully limited browser automation.
Action limits, human approval for high-risk operations, detailed logs and audit trails, prompt-injection defences and encryption. Our certified service processes and governance work help you map the agent to SOC 2, ISO 27001 and AI-specific frameworks.
Yes. Retainers cover monitoring, prompt and policy tuning, new capabilities and model updates as your business and the models change.
Limits that match the risk of each action, a person in the loop where mistakes are expensive, and a log good enough to reconstruct what the agent did and why. We decide those limits with you in the design phase, before any code is written, and test them before release.
| What the agent does | Example | Controls we put in place |
|---|---|---|
| Reads and summarises | Answers staff questions from internal policies | Read-only access, cited sources, logging of every query and answer |
| Drafts for a person | Prepares customer replies or reports | A person reviews and sends; the agent can't send on its own |
| Acts within limits | Updates CRM records, books meetings, routes tickets | Scoped permissions, thresholds above which a person approves, rollback, full action log |
| Touches money or regulated decisions | Refunds, KYC checks, credit or eligibility steps | Human approval on every action, separate audit trail, red-team testing before release |
A pilot proves the agent can do the job on your real workflow. Production adds integrations, limits and monitoring, and only happens if the pilot earns it.
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days.
Thirty minutes, free. The workflow, the systems involved, what the agent may and may not do, and how you'll judge success.
Scope, risk level, controls, schedule and pricing for a pilot.
One workflow end to end on real data, with logging from day one and a measured result at the end.
Integrations, permissions, approval steps, red-teaming and monitoring, scoped as a project.
A maintenance retainer or a dedicated team, ready in 2–4 weeks, keeps the agent tuned as models and workflows change.
Inputs, tool calls, decisions and outputs are recorded, so you can answer "why did it do that?" with evidence.
For regulated work we map the agent to the EU AI Act, NIST AI RMF or Nepal Rastra Bank AI guidance. See AI governance and security.
Monitoring, alerting and change control through our ISO/IEC 20000-1 certified processes. For the platform side, see MLOps and AI infrastructure.
Automated evidence collection and risk triage in a product we built:
We built Certifyi, an AI SaaS platform for governance, risk and compliance, from product architecture to deployment: Go, Next.js and PostgreSQL on AWS, with CI/CD in GitHub Actions. It maps 100+ compliance frameworks in one system and automates evidence collection. The case study reports up to 3x less manual compliance work for its users.
Tell us the use case, the tools involved and the limits you need. We'll come back within two working days with an agent design, a delivery plan and an engagement model.
Dignep Group Pvt. Ltd. is Nepal's ISO/IEC 20000-1:2018 certified software development company. We've delivered 100+ projects for startups and enterprises through dedicated teams, outsourcing, and AI solutions
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