AI agents go beyond answering questions - they carry out multi-step work: reading documents, pulling from systems, drafting outputs, and routing them for approval. ALINEDS designs autonomous multi-agent systems and AI-driven workflow and document automation for the high-volume, rules-heavy processes that slow government and regulated organizations down. Every agent runs inside human-in-the-loop checkpoints with full audit trails, so autonomy never means loss of control. Our in-production system, Hermes, automates end-to-end RFP response - and beyond it we've built a library of deployment-ready MVP agents for public agencies across education, public health, government services, transit, and utilities.
Why it matters
Public-sector and regulated teams spend enormous effort on repetitive, document-heavy work - intake, review, drafting, routing - that's too nuanced for rigid scripts but well suited to governed AI agents. Industry analysts expect many agentic AI projects to fail on unclear value and weak controls; the ones that succeed pair real process fit with human oversight and auditability from day one. That's exactly how we build them.
What you get
Autonomous multi-agent systems
Agents that coordinate across your tools and data to complete multi-step tasks, not just single answers.
Document & workflow automation
Automate intake, extraction, drafting, and routing for high-volume processes.
Human-in-the-loop control
Approval gates at every consequential step, so a person stays accountable for decisions that matter.
Audit trails & observability
Every agent action is logged and traceable for accountability and compliance.
Integration with your systems
Agents work with the systems of record and tools your teams already use.
Governed rollout
We pilot with oversight, measure results, and scale only what works.
How it works
Map
Break the process into steps, decisions, and hand-offs.
Design
Define the agents, their tools, guardrails, and human checkpoints.
Pilot
Run with humans in the loop, measuring accuracy and outcomes.
Scale
Expand what proves out, with monitoring and audit trails in place.
Where it fits
Student Success Advisor · Higher Ed & K-12
Tracks grades and attendance to spot learning gaps, sends early alerts to teachers, and suggests tailored study plans.
Campus Safety Dispatcher · Higher Ed & K-12
Monitors security cameras and emergency calls, alerts campus police, and maps the fastest route to an incident.
IEP Compliance Tracker · K-12
Reviews special-education paperwork, flags missing deadlines, and helps staff write compliant legal goals.
Outbreak Monitor · Public Health
Scans ER logs and pharmacy sales for unusual illness spikes and alerts health workers to potential outbreaks early.
Vaccine Scheduler · Public Health
Helps residents find nearby vaccinations, books appointments, and sends reminders for second doses and seasonal boosters.
Permit Reviewer · State & Local Gov
Reads digital building plans against local zoning rules, approves simple remodeling requests, and flags code violations for human review.
Citizen Service Assistant · State & Local Gov
Answers local questions about trash pickup, tax deadlines, and park hours through a 24/7 web chat.
Traffic Signal Optimizer · Transit
Adjusts signal timing in real time from live bus and car counts to cut gridlock and speed up transit.
Fleet Maintenance Planner · Transit
Tracks mileage and wear sensors on city buses and schedules repairs before a part fails on the road.
Grid Load Balancer · Utilities
Predicts power demand from weather and time of day and shifts loads to prevent blackouts during extreme heat or cold.
Key distinctions
AI agent vs. traditional automation (RPA)
| Aspect | AI agent | Traditional automation (RPA) |
|---|---|---|
| Handles ambiguity | Interprets unstructured input | Breaks on anything unexpected |
| Scope | Multi-step, reasoned tasks | Fixed, scripted steps |
| Documents | Reads and understands content | Needs rigid templates |
| Adaptation | Adjusts to new cases | Requires re-scripting |
| Oversight | Human-in-the-loop gates | Rule-based only |
Compliance & security
Autonomous, but accountable
Agentic systems are built with human-in-the-loop checkpoints, least-privilege access to your systems, and full audit logging on every action. Governance aligns to the NIST AI Risk Management Framework and NIST CSF 2.0 / 800-53, so autonomy stays accountable and every step is traceable for audit.
- NIST AI RMF
- NIST 800-53
- NIST CSF 2.0
- FedRAMP
- GovRAMP
- HIPAA
- FERPA
Key terms
- AI agent
- An AI system that can plan and carry out multi-step tasks toward a goal - using tools and data - rather than only responding to a single prompt.
- Multi-agent system
- Several specialized agents coordinating to complete a larger task, each handling part of the work.
- Human-in-the-loop
- A checkpoint where a person reviews or approves an agent's action before it proceeds.
Frequently asked
What's the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent carries out multi-step work toward a goal - reading documents, using tools, drafting outputs, and routing for approval. Agents act; chatbots respond.
How do you keep autonomous agents from making unchecked decisions?
Human-in-the-loop approval gates at every consequential step, least-privilege access, and full audit trails. Autonomy is bounded, so a person stays accountable for decisions that matter.
Which government processes are a good fit for agentic automation?
High-volume, rules-heavy, document-driven processes - intake, review, extraction, drafting, and routing - where consistency and auditability matter. RFP response, case processing, and records workflows are common starts.
Do you actually have agentic AI running in production?
Yes. Our in-production system, Hermes, automates end-to-end RFP response - real operational agentic AI, not a prototype. We have also built deployment-ready MVP agents for a range of public-sector use cases.
How is this different from the RPA/automation we already have?
Traditional automation follows fixed scripts and breaks on anything unexpected. AI agents interpret unstructured input and reason across steps, so they handle the nuanced, document-heavy work rigid rules can't.
What happens when an agent hits a case it can't handle?
It escalates to a human rather than guessing. We design the exception paths and checkpoints so uncertain or high-stakes cases go to a person by default.
How do you measure whether it is working?
We define success metrics up front - accuracy, completion rate, time saved, exception rate - and pilot with humans in the loop before scaling, so value is proven, not assumed.