AI Agents & Automation

Overview

AI agents go beyond chatbots — they plan, take actions, and close loops without you having to click through every step. I design and build AI agents that integrate with your existing tools (email, CRMs, databases, APIs, Slack) and automate repetitive, multi-step workflows.

The key difference from simple automation: agents can handle ambiguity and variation — they read context, make decisions, and adapt — unlike rigid if/then scripts.

What Agents Can Do

Communication & Triage

• Read incoming emails or support tickets, classify by urgency and topic
• Draft context-aware replies for human approval before sending
• Route tickets to the right team or escalate based on content
• Send automated follow-ups on a schedule

CRM & Data Operations

• Create, update, or close records in your CRM based on events
• Enrich lead data by researching company and contact information
• Score leads based on email content and behavior
• Sync data between disconnected systems

Reporting & Summaries

• Pull data from multiple sources (databases, APIs, spreadsheets)
• Generate weekly performance summaries as Slack messages or email reports
• Summarize long documents, call transcripts, or chat histories
• Create structured reports from unstructured inputs

Workflow Automation

• Trigger downstream actions after a form is submitted or an event fires
• Multi-step approval flows — AI drafts, human reviews, AI executes
• Schedule recurring tasks (daily reports, weekly syncs, monthly audits)
• Monitor dashboards and alert when metrics cross thresholds

How It Works

Planning & Reasoning

Agents use LLMs (GPT-4o, Claude) to break down goals into steps and decide which tool to call next — similar to how a human would think through a task.

Tool Use

Agents are given a set of tools they can call:

• API calls — GET/POST/PATCH to your internal or third-party APIs
• Database queries — read and write records
• Email tools — read, draft, send (with human approval gates)
• Web search — gather external context
• File tools — read CSVs, PDFs, or write reports

Memory & Context

• Short-term memory — the agent remembers context within a session
• Long-term memory — key facts stored in a vector database for future sessions
• Conversation summaries to handle long workflows within token limits

Tech Stack

• OpenAI GPT-4o with function calling / tools — primary agent brain
• Anthropic Claude — for long-context reasoning tasks
• LangChain / LangGraph — for multi-step agent orchestration
• Node.js (Express) or Python (FastAPI) — agent backend
• Bull / BullMQ — job queues for scheduled and background agent runs
• Redis — session state and short-term agent memory
• PostgreSQL + pgvector — long-term memory and semantic search
• Integrations: Slack, Gmail, HubSpot, Airtable, Notion, Twilio, Zapier webhooks

Safety & Control

Agents are powerful — so safety is built in from the start:

• Dry-run mode first — every agent logs what it would do before going live
• Human-in-the-loop gates — sensitive actions (sending emails, deleting records) require approval
• Permission scoping — each agent only has access to the tools it needs
• Audit log — full record of every action taken, with timestamps and reasoning
• Kill switch — pause or disable any agent instantly without code changes
• Rate limiting — prevent runaway loops or accidental mass operations

Delivery & Deployment

• Full specification of agent goals, tools, and constraints before build
• Sandbox environment with synthetic data for safe testing
• Deployment to Render, Railway, Fly.io, or your own VPS
• Scheduled runs via cron or event-driven triggers (webhooks)
• Monitoring dashboard — agent run history, success/failure rates, action log
• Handover documentation — how to add new tools, adjust behavior, and monitor performance