Artificial
intelligence
(AI)
continues
to
reshape
industries,
from
logistics
to
health
care,
but
with
this
transformation
comes
a
steep
learning
curve
for
in-house
legal
teams.
Two
key
concepts
—
AI
Agents
and
Agentic
AI
—
are
central
to
navigating
the
legal
challenges
and
opportunities
this
technology
presents.
While
both
terms
describe
AI
applications,
their
distinctions
are
critical
when
crafting
governance,
compliance,
and
liability
strategies.
Here’s
a
breakdown
of
what
these
terms
mean,
how
they
differ,
and
the
key
legal
issues
that
in-house
lawyers
should
prioritize.
The
Basics:
AI
Agents
Versus
Agentic
AI
AI
Agents
are
task-focused
tools
designed
to
automate
repetitive
processes
or
execute
predefined
instructions.
They
do
not
make
independent
decisions
but
instead
operate
within
the
parameters
set
by
developers.
Examples
include
a
chatbot
handling
basic
customer
service
inquiries
and
tools
like
Gmail’s
Smart
Compose,
suggesting
responses
based
on
context.
In
contrast,
Agentic
AI
is
far
more
autonomous.
These
systems
perceive
their
environment,
reason
through
complex
scenarios,
make
decisions,
and
adapt
over
time.
Unlike
AI
Agents,
Agentic
AI
does
not
require
constant
human
input
to
function.
Examples
include
autonomous
vehicles
navigating
traffic
in
real-time
and
AI
cybersecurity
systems
detecting
and
mitigating
threats
without
manual
oversight.
Think
of
AI
Agents
as
rule-followers
and
Agentic
AI
as
problem-solvers.
Why
The
Distinction
Matters
For
in-house
lawyers,
distinguishing
between
these
AI
types
is
not
just
semantics
—
it
informs
how
you
assess
risks,
ensure
regulatory
compliance,
and
allocate
liability.
Here’s
why:
-
Operational
Scope.
AI
Agents
typically
perform
predictable,
low-risk
tasks,
while
Agentic
AI’s
autonomy
introduces
complexities
like
unexpected
outcomes
and
evolving
behavior. -
Liability.
When
an
AI
Agent
makes
an
error,
it’s
usually
easy
to
trace
responsibility
to
its
operator
or
developer.
With
Agentic
AI,
which
learns
and
adapts,
pinpointing
fault
is
far
more
challenging. -
Compliance.
Regulatory
frameworks,
such
as
the
EU
AI
Act,
often
impose
stricter
requirements
on
autonomous
systems
(Agentic
AI)
due
to
their
higher
risk
profiles.
Understanding
these
differences
ensures
that
your
legal
strategies
are
tailored
to
the
type
of
AI
in
question.
Real-World
Applications
And
Legal
Concerns
AI
Agents
In
Practice
-
Customer
Support.
AI-powered
chatbots
streamline
support
but
can
raise
issues
like
inaccurate
responses
or
biased
interactions.
Legal
teams
must
ensure
compliance
with
consumer
protection
laws. -
Personal
Assistants.
Tools
like
Alexa
and
Siri
perform
helpful
but
limited
tasks.
Data
privacy
concerns
are
prevalent,
as
these
systems
often
handle
sensitive
user
data.
Agentic
AI
In
Practice
-
Health
Care.
Agentic
AI
systems
analyze
complex
medical
data
to
assist
in
diagnoses.
Errors
could
lead
to
malpractice
claims,
raising
questions
about
liability
and
standard
of
care. -
Autonomous
Vehicles.
These
systems
operate
independently,
often
making
life-and-death
decisions.
Liability
for
accidents
is
a
major
legal
gray
area,
implicating
manufacturers,
developers,
and
possibly
regulators.
Top
Legal
Issues
to
Consider
Liability
Frameworks
For
AI
Agents,
liability
is
usually
straightforward
—
often
tied
to
the
deploying
company.
However,
with
Agentic
AI,
where
systems
operate
autonomously
and
evolve
over
time,
liability
can
become
fragmented.
Key
considerations
include
drafting
clear
indemnification
clauses
in
vendor
agreements,
requiring
ongoing
audits
of
AI
system
performance,
and
addressing
cross-jurisdictional
liability
when
systems
operate
internationally.
Regulatory
Compliance
Emerging
regulations,
like
the
EU
AI
Act,
differentiate
between
AI’s
risk
levels.
For
high-risk
applications
like
Agentic
AI
in
health
care
or
transportation,
compliance
requirements
may
include
transparent
documentation
of
the
AI’s
decision-making
processes,
incorporation
of
human
oversight
mechanisms,
and
regular
assessments
for
bias
and
safety.
Ethical
Considerations
Agentic
AI
introduces
significant
ethical
questions,
such
as:
how
to
address
biases
that
AI
systems
might
develop
autonomously,
and
whether
AI
decisions
can
be
explained
in
a
way
that
satisfies
stakeholders
and
regulators.
Data
Privacy
Both
AI
types
rely
heavily
on
data,
raising
risks
under
privacy
frameworks
like
GDPR
or
CCPA.
Ensure
that
consent
is
obtained
for
data
collection,
that
systems
have
robust
cybersecurity
measures,
and
that
AI
Agents
handling
sensitive
data
comply
with
sector-specific
privacy
laws
(e.g.,
HIPAA
for
healthcare).
IP
Protection
AI
systems
can
create
original
outputs,
from
artwork
to
software
code.
Legal
teams
must
evaluate
whether
these
outputs
qualify
for
intellectual
property
protection
and
address
potential
copyright
infringement
risks.
Actionable
Steps
For
In-House
Counsel
To
effectively
manage
AI’s
legal
and
ethical
challenges,
consider
the
following:
-
Develop
Tailored
Contracts.
Address
unique
risks
for
each
AI
type,
specifying
liability,
audit
rights,
and
compliance
obligations. -
Implement
Governance
Policies.
Establish
internal
frameworks
for
the
ethical
use
of
AI,
focusing
on
transparency,
accountability,
and
risk
mitigation. -
Engage
Stakeholders.
Involve
cross-functional
teams
—
including
IT,
risk
management,
and
compliance
—
to
ensure
holistic
oversight
of
AI
systems. -
Monitor
Evolving
Laws.
Stay
ahead
of
AI-specific
legislation,
particularly
in
high-risk
sectors
like
transportation,
healthcare,
and
finance.
Looking
Ahead
AI
Agents
and
Agentic
AI
are
rapidly
advancing,
with
both
offering
tremendous
potential
—
and
unique
legal
challenges
—
for
businesses.
As
the
distinction
between
these
systems
blurs,
legal
teams
must
remain
agile,
ensuring
that
their
organizations
leverage
AI
responsibly
while
protecting
against
liabilities.
For
deeper
insights
into
how
in-house
lawyers
can
navigate
these
complex
issues
while
driving
innovation,
my
book,
“Product
Counsel:
Advise,
Innovate,
and
Inspire,”
offers
practical
guidance.
From
crafting
proactive
legal
strategies
to
fostering
cross-functional
collaboration,
it
equips
counsel
to
address
the
challenges
of
AI
and
other
cutting-edge
technologies
with
confidence
and
creativity.
How
is
your
company
adapting
to
the
rise
of
AI?
Have
you
encountered
unexpected
legal
challenges?
Let’s
discuss
—
share
your
experiences
and
insights.
Olga
V.
Mack
is
a
Fellow
at
CodeX,
The
Stanford
Center
for
Legal
Informatics,
and
a
Generative
AI
Editor
at
law.MIT.
Olga
embraces
legal
innovation
and
had
dedicated
her
career
to
improving
and
shaping
the
future
of
law.
She
is
convinced
that
the
legal
profession
will
emerge
even
stronger,
more
resilient,
and
more
inclusive
than
before
by
embracing
technology.
Olga
is
also
an
award-winning
general
counsel,
operations
professional,
startup
advisor,
public
speaker,
adjunct
professor,
and
entrepreneur.
She
authored Get
on
Board:
Earning
Your
Ticket
to
a
Corporate
Board
Seat, Fundamentals
of
Smart
Contract
Security,
and Blockchain
Value:
Transforming
Business
Models,
Society,
and
Communities. She
is
working
on
three
books:
Visual
IQ
for
Lawyers
(ABA
2024), The
Rise
of
Product
Lawyers:
An
Analytical
Framework
to
Systematically
Advise
Your
Clients
Throughout
the
Product
Lifecycle
(Globe
Law
and
Business
2024),
and
Legal
Operations
in
the
Age
of
AI
and
Data
(Globe
Law
and
Business
2024).
You
can
follow
Olga
on
LinkedIn
and
Twitter
@olgavmack.