AI Agent Development

Focused AI agents designed to automate one specific workflow reliably at production scale: a research agent that queries multiple data sources, synthesizes findings, and delivers a structured briefing without hallucinating sources; a data processing agent that reads incoming records, extracts and transforms specific fields, validates against business rules, and posts clean data to a target system; or a follow-up communications agent that retrieves CRM context, drafts personalized outreach, and queues for send or approval. Each agent is designed around defined inputs, defined tools, defined success criteria, and an evaluation framework built from real production examples -- so you know the agent is working correctly before deployment and you can detect when it degrades afterward. Scope limitations are explicit: the agent knows what it can handle and routes the rest to humans.

Implementation uses the ReAct (Reasoning + Acting) pattern for agents that need to interleave reasoning with tool execution, and Plan-and-Execute for agents where a full plan is better assembled upfront before any tool calls run. Tool schemas are designed in JSON Schema with tight input constraints, explicit examples, and descriptions of what each tool returns and does not return -- the single most effective technique for reducing hallucinated tool arguments in production. Max iteration guards (typically 15-25 steps) prevent runaway loops from consuming budget or getting stuck. Conversation history trimming with a sliding context window keeps token costs predictable. When an agent hits its iteration limit or encounters an unrecoverable tool failure, it escalates to a human operator with a structured summary of what it completed, what it failed on, and what input would allow it to resume. LangSmith or Langfuse tracing captures the full reasoning trace for every run.

Orchestrated systems where multiple specialized agents collaborate on tasks that exceed the reliable capability of a single agent. An orchestrator agent receives the top-level goal, decomposes it into subtasks, and routes each to the appropriate specialist. Parallel worker agents execute their subtasks simultaneously -- reducing total latency for tasks that don't have sequential dependencies. A validation agent reviews primary outputs against defined quality criteria before they proceed downstream, and a synthesis agent combines results from multiple workers into a coherent final output. LangGraph is our primary orchestration framework for stateful multi-agent systems -- it handles the state machine, agent handoffs, and conditional branching that custom orchestration code often implements inconsistently. Used when a single-agent architecture fails on diversity of reasoning required or when throughput requirements need parallel execution.

LangGraph models the multi-agent system as a directed graph with typed state that persists across nodes. Each node is an agent or a tool-execution step; edges define the conditional routing logic. State is checkpointed to PostgreSQL or Redis after each node, so a system failure mid-workflow resumes from the last completed step rather than restarting from scratch -- critical for long-running workflows that touch multiple external systems. Tool call parallelism runs independent subtasks as concurrent graph branches, merging results at a join node before proceeding. Human-in-the-loop interruption points are defined as graph edges that pause execution and write the pending state to a review queue; a human approves or modifies the agent's proposed action, and execution resumes from the checkpoint. Agent trajectory scoring with LangSmith evaluators assesses whether the agent took the minimum necessary steps to complete the task -- a proxy metric for reasoning efficiency and a signal for prompt or architecture improvements.

Agents equipped with the specific tools required to complete their assigned tasks: SQL database query tools with schema-aware validation to prevent injection and ill-formed queries; REST API tools with structured input schemas and error handling for the 4xx and 5xx cases that inevitably occur in production; and web search via Brave or Serper for agents that need current information beyond their training data. File read and write handles document processing workflows. Calendar and email access (via Google Workspace or Microsoft 365 OAuth) supports scheduling agents. CRM/ERP integrations (Salesforce, HubSpot, NetSuite) connect agents to your business systems. Tool definitions are designed to minimize hallucinated arguments -- the most common production failure in tool-using agents -- by providing tight input schemas, example values, and explicit descriptions of what the tool returns and doesn't return. Agents operate within your existing systems rather than requiring separate data pipelines.

AI agents that process documents end-to-end through workflows that combine multiple reasoning steps: extract structured fields from unstructured contracts using function calling with a reasoning model; validate extracted fields against business rules and cross-reference external data sources (party names against your CRM, amounts against approved budgets); and classify document category for downstream routing. The agent then identifies non-standard clauses or anomalies that require human review and triggers the appropriate downstream action (update a record, generate a notification, route to a specialist queue). Unlike simpler OCR pipelines, document processing agents handle the contextual interpretation that template-based extraction fails on -- understanding that "net 30 from invoice date" and "payment due 30 days after delivery" are different payment terms with different implications, and flagging accordingly. Confidence signals at field level route uncertain extractions to human review with the specific uncertainty identified.

AI agents embedded in customer-facing products where the agent is the primary interaction layer. Support agents resolve queries by retrieving and reasoning over order history, product documentation, and account data without human involvement for standard questions. Onboarding agents guide new users through multi-step setup workflows, adapting instructions to the user's specific configuration and product edition. Qualification agents gather information through conversation, assess fit against your criteria, and route to the right team with a structured summary. Conversational interfaces deploy over web chat (streaming responses with Vercel AI SDK or similar), mobile, or voice. Context preservation across a session handles multi-turn conversations where the user references earlier parts of the exchange. Escalation to human agents transfers the full conversation context so the agent doesn't need to re-explain what they already told the bot.

Production observability for deployed agents implemented via LangSmith, Langfuse, or a custom telemetry layer: complete trace logging of every reasoning step, tool call with inputs and outputs, and decision made within each agent run -- the audit trail that lets you debug a production failure by replaying exactly what the agent did. Cost-per-run tracking at the agent level and aggregated across your agent fleet, with alerts when cost anomalies indicate a runaway agent or an unexpected input distribution. Latency monitoring at the step level identifies which tool calls or reasoning steps are contributing most to end-to-end runtime. Evaluation test suites covering the representative range of inputs your agent handles in production -- not just the happy path but the edge cases that reveal brittle behavior. Automated evaluation runs on a schedule to detect quality degradation from model updates or prompt drift before users report it.