Understanding the Role and Capabilities of AI Agents

The
concept
of
AI
agents
has
become
a
pivotal
topic
in
the
field
of
artificial
intelligence,
particularly
in
the
development
of
Large
Language
Models
(LLMs).
According
to
the
LangChain
Blog,
the
definition
and
understanding
of
what
constitutes
an ‘agent’
can
vary
widely,
often
leading
to
confusion
and
debate
among
developers
and
researchers.

Defining
an
AI
Agent

LangChain
Blog
describes
an
agent
as
a
system
that
utilizes
an
LLM
to
determine
the
control
flow
of
an
application.
This
definition,
while
technical,
may
not
align
with
the
common
perception
of
agents
as
advanced,
autonomous
entities.
The
blog
highlights
that
even
simple
systems
where
an
LLM
routes
between
different
paths
can
be
considered
agents
under
this
definition.

Andrew
Ng,
a
prominent
figure
in
AI,
suggests
that
instead
of
debating
which
systems
qualify
as
true
agents,
it
is
more
productive
to
view
agent
capabilities
on
a
spectrum.
This
perspective
aligns
with
how
autonomous
vehicles
are
categorized
by
their
levels
of
autonomy.

The
Spectrum
of
Agentic
Behavior

LangChain
Blog
further
elaborates
on
the
concept
of ‘agentic’
behavior,
presenting
it
as
a
measure
of
how
much
an
LLM
determines
a
system’s
actions.
The
blog
categorizes
systems
into
different
levels
of
agentic
behavior:

• 
  Router:
  Systems
  that
  use
  an
  LLM
  to
  route
  inputs
  into
  specific
  workflows.
• 
  State
  Machine:
  Systems
  that
  include
  multiple
  routing
  steps
  and
  can
  loop
  until
  a
  task
  is
  complete.
• 
  Autonomous
  Agent:
  Highly
  agentic
  systems
  that
  build
  and
  remember
  tools
  for
  future
  steps,
  akin
  to
  the
  implementation
  seen
  in
  the
  
  Voyager
  paper.

This
technical
gradation
helps
developers
design
and
describe
LLM
systems
more
effectively.

The
Importance
of
Agentic
Systems

Understanding
the
level
of
agentic
behavior
in
a
system
can
significantly
influence
the
development
process.
More
agentic
systems
require
robust
orchestration
frameworks,
durable
execution
environments,
and
comprehensive
evaluation
and
monitoring
tools.
LangChain
Blog
emphasizes
that
as
systems
become
more
agentic,
they
also
become
more
complex
and
challenging
to
manage,
necessitating
specialized
tools
and
infrastructure.

For
instance,
highly
agentic
systems
benefit
from
frameworks
that
support
branching
logic
and
cycles,
enabling
faster
development.
They
also
require
monitoring
tools
that
allow
developers
to
observe
and
modify
the
agent’s
state
or
instructions
in
real
time,
ensuring
the
system
stays
on
track.

New
Tooling
for
Agentic
Systems

The
increasing
complexity
and
capabilities
of
agentic
systems
have
driven
the
need
for
new
tools
and
infrastructure.
LangChain
has
developed

LangGraph
for
agent
orchestration
and

LangSmith
for
testing
and
observability
of
LLM
applications.
These
tools
are
designed
to
support
the
unique
requirements
of
highly
agentic
systems.

As
the
field
of
AI
continues
to
evolve,
understanding
and
leveraging
the
spectrum
of
agentic
capabilities
will
be
crucial
for
developing
efficient
and
robust
LLM
applications.

Image
source:
Shutterstock