AI Agent Development Services

A chatbot answers a single question with a single response. An AI agent reasons over multiple steps, calls tools, reads and writes data, and works toward an outcome that may take seconds or hours. An agent has memory, state, and the ability to act on systems, not just talk.

Agentic AI is the design pattern where AI systems take multi-step actions toward a goal using tools, data sources, memory, and workflow state. In business settings, the important question is not autonomy for its own sake. It is whether the system can act inside clear boundaries, with logging, approvals, and cost controls.

LangGraph fits process automation: when steps are predefined, every transition matters, and audit and reliability are critical. OpenClaw and similar autonomous agents fit open-ended work where the agent needs to choose the next move, inside a sandboxed environment. Many workflows should start simpler, with normal code, a scheduler, API calls, and a model only where it adds value.

Agents run inside your cloud or in an environment you control. Data access is scoped with service accounts, row-level security, and read-only by default. We never use a prompt as a security boundary: authorization is enforced deterministically at the tool and data layer, each agent gets its own workload identity and a kill switch, and we test against prompt injection and the OWASP GenAI risk list. For sensitive deployments we use isolated VMs, network policies, and human approval before any write. Our processes align with GDPR.

Yes. The agents we build are grounded on your existing semantic layer: Power BI datasets, Tableau data sources, dbt models, BigQuery views, or Snowflake schemas. The agent consumes the same governed metrics your team already trusts.

In our experience, no. They remove repetitive work and accelerate first drafts so your team focuses on judgement, customer-facing decisions, and edge cases. Our default design keeps a human in the loop for any action that has real business impact.

A focused agent for one workflow, grounded on existing data, typically goes from discovery to a supervised production pilot in 4 to 8 weeks. Broader agentic systems with multiple integrations and approval workflows take longer.

We are model-agnostic. We use Google Gemini models through the Gemini API, Anthropic Claude, and OpenAI models including GPT Image 2 for image generation. We pick per task, often mixing a high-capability model for reasoning with a smaller, cheaper model for routine steps. The model provider should support the architecture, not define it.

A simple rule settles most of it: facts belong in databases and retrieval, behavior belongs in prompts or model weights, and permissions belong outside both. Company knowledge that changes, needs citations, or has access rules should be served through RAG and governed tools, never trained into a model. Fine-tuning earns its place only for narrow, high-volume tasks, often by distilling a frontier model into a smaller, cheaper one after the workflow is proven. Most teams need better retrieval and tools, not a custom model.

We build an evaluation dataset before we optimize anything: straightforward questions, hard multi-document cases, questions that should be refused, and every failure found in testing or production. We score retrieval and generation separately, so we can tell whether a wrong answer came from missing evidence or from the model ignoring good evidence, and check tool calls against expected and forbidden actions. That suite runs on every prompt or model change and blocks the release if critical cases regress.

Model Context Protocol is the emerging standard for connecting AI to tools and data. Instead of wiring each assistant to each system, you build one MCP server that exposes narrow, governed capabilities, such as get_order_status or query_approved_metrics, rather than raw database credentials. Claude, ChatGPT, and your own agents can all use it, with authorization and audit enforced at the tool layer. We build MCP servers over warehouses, ERPs, CRMs, and internal APIs.

We set per-run and per-day budgets, cache tool calls and embeddings, and route routine steps to smaller models. We monitor token usage in dashboards alongside business outcomes so cost is always visible against value.

Yes. We often work alongside in-house teams: we design the architecture, the guardrails, and the evaluation framework, then either implement end to end or hand it off with documentation and training.

We offer two engagement models with transparent pricing.

We also offer project-based pricing for well-defined engagements.

Contact us to discuss the best fit for your needs.