AI Agents That
Do Real Jobs.
Not a chatbot. Not a dashboard. Agents embedded in your CRM and data systems that research, score, route, respond, and act around the clock without a human in the loop. This is digital labor: AI workers built into your platform, executing tasks your team currently does by hand.
Digital Labor Is Not What Most Vendors Are Selling
The market uses the word AI loosely. Most products called AI agents are not. Here is the practical distinction that matters for your business.
A Chatbot
Answers questions based on a knowledge base. Responds to inputs from a human. Does nothing autonomously. Does not update records, trigger workflows, generate deliverables, or take any action unless a human types something. Useful as a support widget. Not digital labor.
An AI Feature
Surfaces AI-generated insights inside a dashboard or tool. Summarizes data, highlights anomalies, suggests next steps. Still requires a human to read the output and decide what to do with it. The human is still the executor. This is a productivity tool, not digital labor.
An AI Agent / Digital Laborer
Acts autonomously without waiting for human input. Executes tasks: updates database records, sends messages, generates structured outputs, calls APIs, applies scoring logic, flags anomalies for review, and routes work to the right place. The human defines the rules and reviews exceptions. The agent does the repetitive work.
Types of Agents IPS Builds
Every category below has been built and deployed in a live product. These are not theoretical architectures.
Research Agents
Pull data from 19 sources simultaneously, synthesize results into a structured dossier, and deliver a finished output in minutes. Lead Detective pulls from LinkedIn, SEC EDGAR, Crunchbase, news, and Glassdoor in parallel. The agent synthesizes everything into an executive dossier and account plan. Eight minutes versus three days of human research.
Scoring Agents
Score every record in your database on a schedule using defined logic. GovGreed runs 13,052 active politician-times-bill predictions through a scoring model inside PostgreSQL in approximately 30 seconds, with no external API calls. The scoring logic lives in the database and runs on a cron schedule. Every record is always current.
Outreach Agents
Generate and send personalized messages based on customer data without human drafting. StickySignal uses purchase history, segment membership, and visit recency to generate AI-written SMS messages for each dispensary customer. The agent handles copy variation, send timing, and compliance checks automatically.
Monitoring Agents
Watch for specific trigger events and take action when they occur. Price changes, SEC filings, news mentions, contract awards, earnings releases. The agent monitors continuously and fires a defined workflow when the trigger condition is met, whether that means logging a record, sending an alert, or kicking off a downstream process.
Classification Agents
Read unstructured text, PDFs, emails, or form submissions and tag, categorize, or route them according to defined taxonomy. Useful for support ticket triage, contract clause extraction, lead qualification from form responses, and document categorization pipelines where the input is messy and the output needs to be structured.
Prediction Agents
Score leads, flag churn risk, predict outcomes, and surface the records most likely to convert or require intervention. mmamodel.ai runs a 5-model ML ensemble retrained weekly against 8,533 UFC fights. The same pattern applies to lead scoring, churn prediction, and deal probability models built on your proprietary data.
Lead Detective: Research Agent in Production
Lead Detective is a live product that demonstrates research agent architecture at commercial scale. Here is exactly how it works.
The agent fires simultaneous requests to LinkedIn, SEC EDGAR, Crunchbase, Google News, and Glassdoor. No waiting for one to finish before starting the next. All five sources run in parallel, returning data in under 90 seconds combined.
Premier AI models receive all source data simultaneously and generate three structured outputs: an executive dossier with company background and leadership profiles, a strategic account plan with opportunity framing, and a conversation guide with specific talking points derived from actual company data.
Results are returned as a structured document a sales rep can read in five minutes and use in a meeting that same day. No researcher. No delay. No source missed because someone ran out of time.
GovGreed: Scoring Agent Inside PostgreSQL
The GovGreed corruption prediction engine scores every politician-bill combination in the database on demand. This is scoring agent architecture at scale, embedded directly in the database layer without external API calls.
The scoring model considers committee membership, stock trade timing relative to legislation, campaign contribution amounts and sources, lobbying relationship mapping, and historical vote patterns. All of this logic runs as a single SQL-level operation inside Supabase. The scoring agent is triggered on a schedule and whenever the underlying data is updated. The dashboard always reflects current scores without any manual analyst work.
How We Embed Agents in Your System
Every agent we build follows a structured deployment process. We do not sell you a black box. We build something you understand and can manage.
We document exactly what a human does today: what triggers the work, what data they look at, what decision they make, and what output they produce.
We identify what event starts the agent, what data sources it reads from, and what the output looks like in structured terms before writing a line of code.
We build the pipeline: data ingestion, transformation logic, AI model calls where needed, output formatting, and error handling with fallback states.
The agent is deployed inside your existing CRM, database, or application. It reads from and writes to your data. No separate vendor portal to manage.
We deploy with logging, success and failure tracking, and alerting. You can see exactly what the agent is doing, when it ran, and what it produced at any time.
What Agents Are Not
Not magic
Agents execute defined logic reliably and at scale. They do not invent solutions to problems that are not well-specified. The quality of agent output depends directly on the quality of the underlying data and the clarity of the process you want automated. Garbage in, garbage out still applies, and we will tell you this upfront if your data is not ready.
Not fully autonomous forever
Agents need supervision. The world changes, data sources change, edge cases appear that were not anticipated. We build agents with monitoring, logging, and exception handling. A human should review exceptions and audit outputs periodically. The goal is to eliminate repetitive volume, not to make humans irrelevant to the process entirely.
Not a replacement for good data
The best agent architecture fails if the underlying data is inconsistent, incomplete, or poorly structured. Before building an agent, we assess whether the data infrastructure can support it. Sometimes the right first project is cleaning and structuring data before any agent gets built. We will tell you this honestly rather than selling you an agent that will fail.
Common Questions
Ready to Add
Digital Workers to Your Stack?
Tell us what your team does by hand today that could be automated. We will tell you whether it is a good fit for an agent and what it would take to build it.
Start an Agent Project →