"AI agents" are everywhere right now — but most businesses still aren't sure what that means.
A practical definition:
> An AI agent is a system that can take actions in a workflow — not just generate text.
It's the difference between:
- a chatbot that answers questions
and
- an agent that can do something: create a ticket, update a CRM, pull a document, trigger a workflow, draft a response for approval, or coordinate multi-step tasks.
This guide explains AI agents in business-friendly terms, with enough technical accuracy to help legal and SaaS teams evaluate when agents make sense — and how to deploy them safely.
What makes something an "agent"?
A system becomes agentic when it has:
1. Goal-oriented behavior (e.g., "resolve this request")
2. Tool use (ability to call APIs or systems)
3. Decision logic (choose actions based on input)
4. Memory / state (track context across steps)
5. Feedback loop (improve based on outcomes or corrections)
Most effective business agents are not "free roaming." They're structured workflows with AI inside — with clear constraints, approvals, and monitoring.
The building blocks of an AI agent
Think of an agent as an orchestrated system that combines reasoning with execution.
1) Perception (understanding input)
The agent interprets:
- user messages
- support tickets
- emails
- documents
- internal data signals
2) Planning (choosing steps)
The agent decides:
- what it needs to do
- which tools to call
- what information is missing
- when to escalate to a human
3) Tool execution (taking action)
Agents can use tools like:
- searching a knowledge base
- pulling customer context from CRM
- opening a ticket in Jira
- creating a draft email
- updating a record in a workflow system
4) Verification (ensuring correctness)
This is one of the most important parts of agent design.
A production agent should:
- verify it retrieved the right data
- confirm permissions before actions
- cite sources when answering questions
- stop when uncertain
Without verification, agents can become unpredictable and risky.
What's the difference between an agent and automation?
A helpful mental model:
- Automation is rules-first ("if X then do Y")
- Agents are intent-first ("here's the goal — determine the steps")
Agents can handle messy real-world inputs and multi-step tasks that are hard to encode as rigid rules — but they also require stronger guardrails.
The safest way to deploy agents: human-in-the-loop
For most legal and SaaS workflows, the best pattern is:
1. Agent drafts output
2. Human approves
3. Agent executes
This prevents common failure modes:
- sending incorrect external emails
- updating the wrong customer record
- misrouting legal requests
- making irreversible changes based on incomplete context
Over time, as confidence grows, you can automate more steps.
Best business use cases for agents (legal + SaaS)
Agents are strongest when tasks are:
- multi-step
- repetitive
- require context gathering
- benefit from automation and standardization
Legal agent use cases
1) Intake triage + routing
- classify contract requests
- extract key metadata
- route to the right reviewer
2) Contract summarization and clause extraction
- summarize key terms
- highlight risks
- produce standardized outputs
3) Policy Q&A with citations
- answer questions from internal policies
- show what sources were used
- reduce time spent searching documents
4) Drafting checklists and playbooks
- generate review checklists
- highlight missing clauses
- propose standard language (for review)
> In legal workflows, keep final decisions and external communications human-reviewed.
SaaS / tech agent use cases
1) Ticket classification + enrichment
- categorize tickets
- detect urgency signals
- pull customer plan and history
- draft structured ticket summaries
2) Onboarding assistant
- guide customers through setup
- answer common integration questions
- trigger internal tasks when blockers appear
3) Internal support agent
- act as a help desk assistant
- search internal docs
- propose answers with citations
4) Sales enablement agent
- summarize account status
- generate outreach drafts
- recommend next actions based on CRM signals
When you shouldn't use agents
Agents are not always the right answer.
Avoid agents when:
- the workflow is simple and rule-based
- the task is high-stakes and requires judgment
- you don't have clear action constraints
- you cannot monitor or audit outputs
- the organization isn't ready to handle exceptions
Sometimes the best solution is:
- deterministic automation + AI summarization
rather than a full agent.
Guardrails you should insist on (non-negotiables)
If an agent can take actions, you need guardrails.
Minimum agent guardrails
- approval steps for external actions
- least-privilege tool access
- logging and audit trails
- confidence thresholds
- safe fallback ("I'm not sure — escalating")
- protections for sensitive data (PII and confidential info)
Especially important for legal workflows
- citations and source transparency
- review requirements for risk decisions
- clear ownership and accountability
What good agent design looks like
A good agent behaves like a disciplined analyst, not a creative writer:
- clear about what it knows vs doesn't
- cites sources when making claims
- uses tools intentionally
- stops and asks for clarification when needed
- escalates appropriately
- improves through feedback
The goal isn't to create "AI that always answers." It's to create systems that produce reliable outcomes.
A simple evaluation checklist
If you're considering agents, ask:
1. What decisions can the agent make safely?
2. What actions should require approval?
3. What tools does it need, and what access?
4. How will outputs be verified?
5. How will you measure success?
6. How will you detect drift and errors over time?
If you can answer those, you can deploy agents responsibly.
Final thoughts
AI agents are powerful, but they're not magic. The organizations that succeed with agents treat them like production systems:
- scoped
- integrated
- monitored
- governed
Start with a workflow where human review is natural, prove ROI, then expand.
Want to explore agents safely in your organization?
Stratus Logic builds agentic systems with guardrails, monitoring, and measurable outcomes.