What is a business AI agent?
A business AI agent is a software system that can plan, reason, and take actions across multiple systems to complete a workflow with minimal human guidance. Unlike a chatbot that answers one question at a time, an agent chains together retrieval, reasoning, tool use, and approval into a repeatable process — with evaluation at the end so it gets better over time. It is the difference between asking a question and delegating an entire workflow.
Business workflows we automate
We start with the business workflow, not the model. Typical engagements target proposals and SOW drafting, document-heavy operations (invoices, contracts, claims), customer-service triage and knowledge synthesis, internal status reporting, exception routing, and discovery-call requirements extraction — workflows where context spans many systems and a small mistake has a real cost.
- Proposal-to-SOW drafting — gather requirements, retrieve past proposals, draft, and route for approval.
- Document-heavy operations — invoices, contracts, claims processing across multiple systems.
- Customer-service triage — classify, retrieve knowledge, suggest responses, and escalate exceptions.
- Internal status reporting — collect signals from project tools, draft summaries, and route to stakeholders.
- Exception routing — detect anomalies, gather context, and assign to the right team with recommended actions.
What makes a workflow agentic
A workflow is a good candidate for agentic AI when several signals are present: multiple steps, multiple systems, unstructured inputs, judgment under recognizable patterns, and outcomes that can be measured. If the work is fully structured and rule-based, deterministic automation is usually a better answer — and we will say so.
- Multiple steps with handoffs between people or systems.
- Context gathered from more than one system of record.
- Unstructured inputs (documents, emails, free-form requests).
- Judgment under recognizable patterns rather than rigid rules.
- Outcomes that can be measured (cycle time, accuracy, cost per item).
Human approval and permission controls
Sensitive actions are gated by explicit human approval. Each agent is scoped to a narrow set of tools and permissions, source-system ACLs are propagated through retrieval so the agent only sees what the requesting user can see, and every action is logged with traceable inputs and outputs.
Tool use and system integrations
Agents connect to the systems your team already uses — email, calendars, document stores, CRM, ERP, ticketing, finance, custom internal tools — via APIs and the Model Context Protocol (MCP). MCP keeps the integration layer portable across models and frameworks so you do not have to rebuild when the agent stack evolves.
Evaluation and observability
Every agent ships with a golden test set (typically 30–100 representative tasks), LLM-as-judge scorers for subjective dimensions, deterministic validators for structured output, CI/CD gates that block deployment on regression, and production trace sampling that grows the eval set over time.
Closed-loop agent architecture
Every agent we build operates on the same loop: capture work and signals → normalize and govern inputs → retrieve permissioned context and memory → reason and plan → take or recommend an action → evaluate the outcome and feed it back. The evaluation step is what keeps the agent improving instead of degrading silently.
Technical stack and frameworks
We are deliberately model- and framework-neutral. Typical builds combine LangGraph or the Anthropic Claude Agent SDK for orchestration, hybrid retrieval (BM25 + dense + reranker), pgvector or Qdrant for memory, MCP for tool use, and Langfuse, LangSmith, Braintrust, or Arize Phoenix for observability — selected based on deployment model and existing stack. We do not force a vendor; we choose the stack that fits the workflow.
Engagement and delivery model
Most agent development engagements follow a 4-phase arc: a 20-minute Workflow Triage to confirm fit, a 1–2 week design phase mapping agent goals and tools, a 3–5 week build phase with weekly demos and eval gates, and a managed launch with production traces and KPI baselining. Fixed-fee pilots start from $50,000.
Proof of concept and maturity
Each example below is labeled by current maturity. We do not publish customer metrics until they are verified.
- Proposal-to-SOW assistant — Demonstration Prototype.
- Discovery-call requirements extraction — Demonstration Prototype.
- Sprint planning agent — Architecture Case Study.
- RFP win-themes assistant — Architecture Case Study.
- Internal case-study drafting — Internal Implementation.