Enterprise AI Agent Development

An agentic AI system does something categorically different from a chatbot or an RPA script: it breaks a goal into a sequence of actions, selects and uses the appropriate tools, maintains memory and context across steps, and operates across multiple enterprise systems simultaneously. A chatbot responds to a prompt within a single channel. An RPA script executes a fixed sequence of steps against a predetermined interface. Where a chatbot answers and an RPA script clicks, an agent reasons, plans, and acts within governance boundaries you define.

Custom development becomes the more defensible choice when your workflows cross system boundaries that no single vendor owns, when your proprietary data requires domain-specific fine-tuning that off-the-shelf products cannot accommodate, or when your compliance posture demands deployment in an environment outside a vendor's shared cloud infrastructure. Platform AI products are engineered to perform within their native ecosystems, such as Copilot in Microsoft 365, Agentforce in Salesforce CRM, and Joule in SAP. For organizations already standardized on a single vendor, they offer a fast, well-supported path to initial value. It's also worth noting that these approaches aren't mutually exclusive; we frequently build on top of Copilot Studio or Azure AI Foundry, extending their reach into the systems they don't natively connect to.

A multi-agent architecture becomes necessary when a workflow spans multiple systems, involves interdependent decisions, or requires parallel execution with separate oversight at each stage — the kind of operational complexity where a single agent would either stall or take on too much responsibility to govern reliably. A single agent is well-suited to contained, bounded tasks. A multi-agent system distributes a complex workflow across several agents, each with a defined, specialized role — a planner that decomposes the objective, executors that carry out discrete actions, validators that verify outputs, and analysts that interpret results.

From the opening Agent Opportunity Assessment to first production deployment, most engagements run 3-6 months, with the range driven by integration complexity, data readiness, and the depth of governance controls required. The Assessment phase itself typically takes 2-3 weeks and is specifically designed to surface scope risks early, before they affect the delivery timeline. In general, task-specific agents with limited system connectivity tend to fall in the 2-4 month range. Complex multi-agent systems with deep enterprise integration and a full governance infrastructure typically take 4-9 months to run.

Pricing reflects agent complexity, the number and nature of system integrations, and the governance architecture required. Here's how engagements typically tier:

Tier 1: Task-specific agents — $60,000–$75,000/2 – 3 months

One or two isolated agents, a foundational RAG pipeline (LlamaIndex/Pinecone), and integration with one or two systems via standard REST APIs. Suited to well-defined, bounded automation: automated contract auditing, intelligent support ticket triage with response generation, or structured data extraction from a single source.

Tier 2: Multi-agent orchestration — $80,000–$160,000/3 – 5 months

A coordinated system of 3–5 specialized agents with complex interaction logic built on LangGraph or CrewAI, advanced Enterprise RAG handling unstructured data and knowledge bases, strict human-in-the-loop controls and kill switches, and native integration with legacy enterprise platforms, like SAP, Salesforce, ServiceNow, via Model Context Protocol. A representative example: an autonomous procurement module that communicates with suppliers, verifies specifications, runs compliance checks, and prepares documents for ERP submission without manual handoffs.

Tier 3: Autonomous enterprise ecosystem — $200,000+/6 months and up

A full-scale multi-agent mesh, custom LLM or SLM fine-tuning on sensitive proprietary data, hybrid or on-premise deployment in an isolated customer VPC using Kubernetes and Helm charts, and full regulatory compliance architecture covering ISO 27001, EU AI Act, and DORA. At this tier, the system is not a tool added to existing operations — it becomes load-bearing infrastructure. Examples include end-to-end compliance and operations automation for financial services, or fully integrated supply chain intelligence for large-scale manufacturing.

Pricing does not include cloud infrastructure costs (AWS/Azure) or LLM API token consumption (OpenAI/Anthropic). Both are actively optimized during the architectural design phase and modeled before go-live.

There's no universal answer: access requirements are scoped to the agent's automation. In practice, most production agents need read and write access to the relevant enterprise systems via APIs, as well as a defined data layer that combines structured records with unstructured content for retrieval. We establish the precise data and permission requirements during the architecture phase, ensuring agents operate with exactly what they need and nothing more.

Through deliberate architecture, not operational optimism. Every production system we build includes confidence thresholds that determine when an agent proceeds versus escalates, approval gates for actions above defined risk levels, override controls for targeted intervention, and kill switches for immediate termination without system-wide disruption. High-stakes actions require explicit human confirmation before execution. Every action, including what was attempted, under what conditions, and with what outcome, is fully logged.

Compliance is an architectural input, not a post-launch checklist. Our delivery process incorporates role-based access controls, strict data isolation between tenants and processes, zero-trust connectivity, immutable audit trails, and explainable decision logging, structured to align with ISO 27001 and ISO 9001, which govern our delivery practice. For regulated deployments, we also design with reference to the EU AI Act's high-risk system classification, DORA operational resilience obligations, and, where relevant, the FCA/PRA frameworks. We note that while we build to accommodate these frameworks, we do not provide legal or regulatory advice — clients should involve their compliance and legal teams throughout the engagement.

Yes. We build AI integrations natively against each platform's API surface using REST, GraphQL, and Model Context Protocol, the emerging open standard for agent-to-tool connectivity. There is no screen scraping, no UI-dependent automation, and no brittle middleware sitting between the agent and the system. When a platform interface changes, the integration doesn't break.

Our systems are built to be model-agnostic from the start, which means an LLM update — swapping one frontier model for another, or routing certain tasks to a smaller specialized model — doesn't require architectural changes. Every production agent is instrumented with observability tooling that continuously tracks performance metrics and output quality. Automated drift alerts are configured to flag degradation before it becomes a business problem rather than after.

Agentic workflows are asynchronous by design. A multi-step process that involves LLM reasoning, tool invocation, and inter-agent coordination typically completes in seconds to minutes, which is precisely the right performance profile for background automation: processing a loan application, working through a batch of contracts, routing a compliance report, or triggering a maintenance order from sensor data. For interactions that demand sub-second responses, such as instant chat, real-time UI, live customer-facing experiences, a simpler AI assistant or traditional application layer is the appropriate architecture. We'll tell you clearly which approach fits your use case during the assessment phase.

We design for cost predictability from the outset: lightweight local models handle extraction and classification tasks; frontier models are reserved for complex reasoning that genuinely requires them. Semantic caching eliminates redundant calls on repeated or structurally similar workflows. Hard token budget caps are enforced at both the agent and workflow level, with automatic fallback to lower-cost models when a threshold is reached. Token expenditure in agentic systems is an engineering problem, not an inevitability. Before go-live, we model expected cost per workflow type against your projected usage volumes, so your FinOps team has a forecast, not a surprise. Post-launch dashboards provide visibility at the individual agent level, making cost attribution clear and optimization decisions straightforward.