AI Agent Development
Custom autonomous AI agents — multi-step task planning, tool use, MCP server integration, and real-world workflow automation built for production reliability.
The Challenge
Building AI Agents That Actually Work in Production Is Hard
The gap between 'look at this AI agent demo' and 'this AI agent runs 24/7 processing real business data reliably' is vast and largely invisible until you are already deep in a project. Agents fail in production for predictable reasons that experience makes avoidable: they loop indefinitely on unexpected inputs, make tool calls with malformed parameters, hallucinate information when context is ambiguous, incur unpredictable costs when not constrained, and generate outputs that look correct but contain subtle errors. Healthcare AI agents carry the additional risk that a confidently wrong output is not a UX problem — it is a clinical risk. I have shipped a production Agentic Medical Director — a Claude-powered agent that autonomously analyses quality metrics for 20,000+ US Skilled Nursing Facilities, identifies care quality gaps, generates QAPI improvement recommendations, and escalates edge cases to human clinicians. It runs continuously, handles tens of thousands of inference requests per day, and has production-level error handling, cost controls, and observability. Building that system taught me where AI agents break and exactly how to design around those failure modes.
Deliverables
AI Agent Development Capabilities
- Single-agent systems — focused autonomous agents for specific workflows: document analysis, data extraction, research, report generation, email triage, or code review
- Multi-agent orchestration — coordinator agents that decompose complex tasks and dispatch to specialist sub-agents, with aggregation and conflict resolution logic
- LangChain and LangGraph workflow design — stateful agent graphs, conditional routing, human-in-the-loop interruption points, and persistent agent memory
- Semantic Kernel agent framework — .NET-native agent development with plugin architecture, planner integration, and memory store connectors
- Model Context Protocol (MCP) server development — build MCP servers that expose your internal tools, APIs, and databases to any MCP-compatible AI client (Claude, Cursor, custom agents)
- Tool definition and call handling — designing the tool APIs that agents use, with schema validation, safe execution, error reporting that agents can recover from, and result formatting optimised for LLM consumption
- Agent memory design — short-term working memory (conversation context), long-term memory (vector store retrieval), and structured memory (database-backed state)
- Prompt engineering for agent reliability — system prompt design, few-shot examples, chain-of-thought structuring, and output format specification that produces consistent, parseable agent outputs
- Agent cost optimisation — model routing (use Haiku for classification, Opus for complex reasoning), prompt caching, output caching, token budget management, and cost-per-task tracking
- Agent observability — trace every step with LangSmith or custom logging, monitor output quality, detect failure patterns, and support systematic improvement over time
- Safety and guardrails — input validation, output filtering, PII detection, clinical claim validation, and human escalation triggers for high-stakes decisions
Stack
Technology Stack
Process
AI Agent Development Process
A clear, predictable engagement model with no surprises.
Workflow Design & Tool Inventory
Map the workflow the agent will execute: every step, decision point, required data source, and action the agent must take. Define every tool the agent needs — API calls, database queries, file operations, user notifications. Design the tool interface specifications before writing a line of agent code.
Failure Mode Analysis
Before building, systematically enumerate the ways this agent can fail: unexpected inputs, tool call failures, ambiguous context, LLM refusals, looping, and cost overruns. Design mitigation for each failure mode. This analysis shapes the architecture more than any other step.
Prototype & Evaluation Framework
Build a minimal prototype and define an evaluation framework — a test suite of representative scenarios with expected outputs and quality criteria. Every subsequent iteration is measured against this framework, making progress objective rather than subjective.
Production Hardening
Add all the things that separate a demo from a production system: retry logic with exponential backoff for transient failures, circuit breakers for external dependencies, cost tracking per execution, complete trace logging, PII handling, and human escalation paths for low-confidence or high-stakes decisions.
Monitoring & Continuous Improvement
Deploy with full observability. Review agent traces weekly. Capture user feedback where applicable. Identify systematic failure patterns and run controlled prompt experiments. Production AI agents improve over time when monitored and iterated — without monitoring, they degrade as the world changes around them.
FAQ
Frequently Asked Questions
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