Custom AI Agents
Put AI to work on tasks that require thinking, not just clicking.
Beyond simple automation - we build AI agents that research, analyze, draft, and execute multi-step tasks with human oversight built in. Think of it as a tireless team member that works while you sleep.
Automation handles the simple stuff. Who handles the rest?
You've probably automated a few things already. Maybe a form that triggers an email. Maybe a Slack notification when a deal closes. That's fine for tasks that follow a straight line - if this, then that.
But most of the work that eats your team's time isn't that simple. It's the research before a sales call that takes 40 minutes of Googling and tab-switching. It's the weekly report that requires pulling data from three tools, spotting the trends, and writing a summary nobody has time for. It's the RFP response that needs to be drafted, customized, fact-checked, and formatted before the deadline.
These tasks require judgment. Context. The ability to look at information from multiple sources and figure out what matters. Traditional automation can't touch them. Your team does them manually, over and over, and it's the most expensive use of their time.
AI agents change the math. They don't just follow rules - they reason through problems, use tools, pull from multiple data sources, and produce real output. Not perfectly. Not without oversight. But well enough that your team reviews and approves instead of doing the entire job from scratch.
That's the difference between automation and an agent. Automation clicks the button. An agent decides which button to click and why.
What we build
Agents that do the job, not just the first step.
Research Agents
Feed it a prospect name, a company, a market, a competitor - and it comes back with a structured brief. Not a wall of raw search results. A synthesized, organized summary with the information your team actually needs. Sales teams use these before calls. Executives use them before meetings. Strategy teams use them to monitor competitive landscapes without dedicating headcount to it.
Content & Drafting Agents
Agents that produce first drafts of real business deliverables - proposals, reports, email sequences, SOPs, case studies, social posts, client updates. They pull context from your CRM, project history, brand guidelines, and past work so the output isn't generic. Your team edits and polishes instead of starting from a blank page every time.
Data Analysis Agents
Point them at your data - sales numbers, campaign performance, customer feedback, survey results, financial reports - and they surface what matters. Trends, anomalies, comparisons, plain-English summaries. They don't replace your analysts. They give everyone else on the team the ability to ask questions about the data and get useful answers without filing a request.
Workflow Orchestration Agents
Agents that manage multi-step processes end to end. A new client signs a contract - the agent creates the project, assigns tasks, drafts the kickoff email, prepares the onboarding checklist, and notifies the team. Not as a rigid automation that breaks when something's slightly different, but as an intelligent coordinator that adapts to context and handles edge cases.
Monitoring & Alert Agents
Agents that watch for things so your team doesn't have to. A competitor changes their pricing - you know about it. A key metric drops below threshold - someone gets pinged with context, not just a number. A client hasn't logged in for two weeks - the account manager gets a heads-up with suggested next steps. Proactive intelligence instead of reactive scrambling.
Custom & Domain-Specific Agents
Your business has processes that are unique to you. Maybe it's a compliance review workflow. Maybe it's a vendor evaluation pipeline. Maybe it's a client reporting cycle that involves pulling data from five sources and formatting it just so. If the process involves gathering information, making decisions, and producing output - we can build an agent for it.
Under the hood
No magic. No mystery. Here's what's under the hood.
An AI agent isn't a chatbot with a fancy name. It's a system with three core components:
A brain.
A large language model (Claude, GPT, or similar) that can reason, follow instructions, and produce coherent output. This is what gives the agent judgment - the ability to read context and decide what to do next.
Tools.
Connections to your real systems - CRM, email, databases, web search, file storage, APIs. The agent doesn't just think about your business. It can reach into your tools, pull real data, take real actions, and produce real deliverables.
Guardrails.
Rules, constraints, and human checkpoints that keep the agent within bounds. It can draft an email but not send it without approval. It can pull financial data but not modify records. It can recommend an action but flag edge cases for human review. The guardrails are what make agents trustworthy, not just capable.
Every agent we build includes logging so you can see exactly what it did, why it did it, and what data it used. No black boxes.
Human in the loop
AI does the work. You keep the control.
Let's be direct about this: we don't build fully autonomous AI that runs your business without human involvement. That's not where the technology is, and even if it were, it's not what most businesses actually need.
What we build is a system where AI handles the 80% - the gathering, the drafting, the analysis, the assembly - and a human handles the 20% that requires judgment, taste, or final sign-off. The result is dramatically less time spent per task without removing human accountability.
Every agent we deploy has configurable checkpoints. You decide where humans review, approve, or override. Some clients want tight oversight - every output reviewed before it goes anywhere. Others are comfortable giving agents more autonomy for low-stakes tasks. We set it up the way that matches your comfort level and adjust over time as trust builds.
How it works
From use case to deployed agent in 2–4 weeks.
Use Case Definition
We identify the tasks that are costing your team the most time and are best suited for an agent. Not everything should be an agent - some things are better handled by simple automation or a human. We help you pick the high-impact, high-frequency tasks where AI reasoning actually moves the needle.
Architecture & Tool Access
We design the agent's workflow - what it needs to know, what tools it needs access to, what decisions it should make versus escalate, and what the output looks like. We configure the model, connect your data sources, and build the tool integrations.
Build & Stress Test
We build the agent, test it against real scenarios, and throw edge cases at it until we're confident it handles them gracefully. We tune the prompts, adjust the guardrails, and refine the output format until it meets your team's standards. You'll see working demos before anything goes live.
Deploy & Improve
We deploy into your environment - Slack, email, internal dashboard, scheduled background tasks, wherever the agent needs to live. Then we monitor performance, gather feedback from your team, and continuously improve. Agents get better over time because we're actively tuning them, not because we set them loose.
Real examples
What this looks like in practice.
Pre-Call Research Agent
The problem: Sales reps spend 30–45 minutes researching each prospect before a call.
The agent: Takes a prospect name and company, pulls data from the web, LinkedIn, CRM, and past email threads, and delivers a structured brief - company overview, recent news, key contacts, past touchpoints, suggested talking points.
The result: Research time drops from 40 minutes to 2 minutes of review.
Weekly Performance Digest
The problem: A manager spends Friday afternoon pulling numbers from the CRM, project tool, and analytics platform.
The agent: Runs every Friday at 3pm. Pulls pipeline data, project status, and key metrics. Generates a written summary highlighting wins, risks, and trends.
The result: A four-hour weekly task becomes a two-minute review.
Proposal Drafting Agent
The problem: Every new proposal starts from scratch. Someone digs through old proposals for language, pulls client details from the CRM, and assembles a document manually.
The agent: Takes the client name, project scope, and a few bullet points. Pulls relevant past proposals, client history, and pricing templates. Produces a formatted first draft that's 70–80% ready.
The result: Proposals go out faster, more consistently, and with less grunt work.
Who this is for
This is a good fit if...
- Your team spends hours on research, reporting, or drafting that follows a repeatable pattern
- You've already automated the simple stuff and you're looking for the next level
- You have people doing high-value work who are bottlenecked by low-value prep tasks
- You want AI that actually does something - not just answers questions in a chat window
- You're comfortable with a human-in-the-loop model where AI does the heavy lifting and your team reviews the output
- You need to scale output without scaling headcount at the same rate
This probably isn't the right service if your tasks are truly one-off and unpredictable with no repeatable pattern, or if you're looking for a fully autonomous system that runs without any human oversight. Agents work best on tasks that happen regularly, follow a general structure, and benefit from reasoning - not on genuinely novel work that changes completely every time.
FAQ
Common questions about custom AI agents.
How is an AI agent different from an automation?
An automation follows a fixed path - if this happens, do that. An agent can reason. It reads context, makes decisions about what to do next, pulls from multiple sources, and produces output that varies based on the situation. Automations handle predictable tasks. Agents handle tasks that require judgment.
Will the agent make mistakes?
Sometimes. AI isn't perfect, and we don't pretend it is. That's exactly why every agent we build includes human checkpoints. The agent does the heavy lifting - gathering, drafting, analyzing - and your team reviews and approves the output before it goes anywhere that matters. Over time, as we tune the agent based on feedback, the error rate drops significantly.
Can agents access our internal tools and data?
Yes - that's the whole point. We connect agents to your CRM, email, project management tools, databases, file storage, and any other system with an API. The agent works with your real data, not generic information. All access is logged and permissioned.
How secure is this? Who can see our data?
Agent interactions are logged and auditable. We follow the security and data handling policies of the AI providers we use (Anthropic, OpenAI) and can configure agents to run within specific data boundaries. For clients with strict compliance requirements, we discuss security architecture during discovery and design accordingly.
What if our needs change? Can the agent be updated?
Absolutely. Agents aren't static - they're designed to evolve. New data sources, different output formats, adjusted guardrails, additional capabilities. Updating an existing agent is far faster than building a new one because the architecture is already in place.
How much does this cost?
It depends on the complexity of the agent - how many data sources it needs, how many tools it connects to, and how sophisticated the reasoning needs to be. A focused single-task agent is a different investment than a multi-step orchestration agent with six integrations. We'll scope it clearly during discovery.
Your team's best work isn't data entry and first drafts.
Book a free 30-minute discovery call. We'll identify the tasks that are eating your team's time, show you what an AI agent could realistically handle, and give you an honest picture of what's worth building. No buzzwords. No demos of things that won't work for your business.
Or reach out directly at hello@highground.ai