Distributed Systems Patterns for Multi-Agent Coordination | Matthew Gribben
Distributed Systems AI Agents Architecture
Distributed Systems Patterns for Multi-Agent Coordination Applying consensus protocols and distributed state management to coordinate autonomous AI agents at scale.
December 5, 2025 4 min read
Multi-agent AI systems face a familiar challenge: coordinating independent entities that must work together toward shared goals. This is distributed systems 101, and decades of research have produced battle-tested solutions. Yet most multi-agent frameworks ignore this wisdom, reinventing wheels that were perfected years ago.
Let's fix that.
The Coordination Problem
When multiple AI agents work together, they face classic distributed systems challenges:
Consensus : Agreeing on shared state
Ordering : Determining sequence of operations
Failure handling : Recovering from agent crashes
Conflict resolution : Handling contradictory actionsSound familiar? These are the same problems that Paxos, Raft, and two-phase commit were designed to solve.
Pattern 1: Leader Election for Agent Orchestration When agents need to coordinate complex tasks, elect a leader:
{
:
(): < > {
leader = . . ()
. . (leader. )
}
( : ): < > {
leader = . ()
plan = leader. (task)
assignments = leader. (plan, . )
results = . (
assignments. ( a. . (a. ))
)
leader. (results)
}
}
Clear responsibility : One agent makes decisions
Automatic failover : New leader elected if current fails
Reduced coordination overhead : Followers just execute
Pattern 2: Event Sourcing for Agent State Don't store agent state directly—store the events that produced it:
{
:
:
:
: | | | |
:
}
{
:
( : ): < > {
. . ({
: ,
: decision,
: . ()
})
}
(): < > {
events = . . ()
events. (applyEvent, )
}
( : ): < > {
events = . . (checkpoint. )
events. (applyEvent, checkpoint. )
}
}
Full audit trail : Every decision is recorded
Time travel debugging : Replay state at any point
Easy recovery : Rebuild state from events
Pattern 3: Saga Pattern for Multi-Agent Transactions When multiple agents must complete related actions atomically:
{
: [] = []
(
: ,
: < >,
: < >
): {
. . ({ agent, action, compensate })
}
(): < []> {
: [] = []
: [] = []
{
( step . ) {
result = step. ()
results. (result)
completed. (step)
}
results
} (error) {
( step completed. ()) {
step. ()
}
error
}
}
}
saga = ()
. (researchAgent,
researchAgent. (topic),
researchAgent. (topic))
. (analysisAgent,
analysisAgent. (data),
analysisAgent. ())
. (writerAgent,
writerAgent. (analysis),
writerAgent. ())
saga. ()
Pattern 4: CRDT for Shared Agent Knowledge When agents need to share and merge knowledge without coordination:
{
: < , > = ()
( : ): {
current = . . (agentId) ||
. . (agentId, current + )
}
( : ): {
merged = ()
( [id, count] . ) {
merged. . (id, . (count, other. . (id) || ))
}
( [id, count] other. ) {
(!merged. . (id)) {
merged. . (id, count)
}
}
merged
}
(): {
. ( . . ()). ( a + b, )
}
}
Offline operation : Agents work independently
Automatic merging : No conflict resolution needed
Eventual consistency : All agents converge
Pattern 5: Circuit Breaker for Agent Failures Prevent cascading failures when agents become unreliable:
{
failures =
?:
: | | =
call<T>( : , : <T>): <T> {
( . === ) {
( . ()) {
. =
} {
(agent. )
}
}
{
result = ()
. ()
result
} (error) {
. ()
error
}
}
(): {
. =
. =
}
(): {
. ++
. = ()
( . >= ) {
. =
}
}
}
Putting It Together Our production multi-agent system uses all these patterns:
┌────────────────────────────────────────────────────┐
│ Control Plane │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────┐ │
│ │ Leader │ │ Event │ │ Circuit │ │
│ │ Election │ │ Log │ │ Breakers │ │
│ └──────────────┘ └──────────────┘ └──────────┘ │
└────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Agent 1 │ │ Agent 2 │ │ Agent 3 │
│ (CRDT) │◄───►│ (CRDT) │◄───►│ (CRDT) │
└─────────┘ └─────────┘ └─────────┘
The result: a system that's fault-tolerant, auditable, and scalable. Not because we invented something new, but because we applied proven patterns.
The best multi-agent systems aren't AI breakthroughs—they're good distributed systems that happen to use AI agents as nodes.
MG
Matthew Gribben Chief Technology Officer writing about AI systems, software architecture, cyber security, cryptography, and the practical realities of technology leadership.
class
AgentCluster
private
raft
RaftConsensus
async
electLeader
Promise
Agent
const
await
this
raft
runElection
return
this
agents
get
id
async
executeTask
task
Task
Promise
Result
const
await
this
electLeader
const
await
planExecution
const
await
assignSubtasks
this
agents
const
await
Promise
all
map
a =>
agent
execute
subtask
return
aggregate
interface
AgentEvent
id
string
agentId
string
timestamp
number
type
'TASK_STARTED'
'OBSERVATION'
'DECISION'
'ACTION'
'RESULT'
payload
unknown
class
EventSourcedAgent
private
eventLog
EventLog
async
recordDecision
decision
Decision
Promise
void
await
this
eventLog
append
type
'DECISION'
payload
timestamp
Date
now
async
getState
Promise
AgentState
const
await
this
eventLog
getAll
return
reduce
INITIAL_STATE
async
replayFrom
checkpoint
Checkpoint
Promise
AgentState
const
await
this
eventLog
getAfter
timestamp
return
reduce
state
class
AgentSaga
private
steps
SagaStep
addStep
agent
Agent
action
() =>
Promise
Result
compensate
() =>
Promise
void
this
this
steps
push
return
this
async
execute
Promise
Result
const
results
Result
const
completed
SagaStep
try
for
const
of
this
steps
const
await
action
push
push
return
catch
for
const
of
reverse
await
compensate
throw
const
new
AgentSaga
addStep
() =>
gatherData
() =>
discardData
addStep
() =>
analyzeData
() =>
discardAnalysis
addStep
() =>
generateReport
() =>
discardReport
await
execute
class
ObservationCounter
private
counts
Map
AgentId
number
new
Map
increment
agentId
AgentId
void
const
this
counts
get
0
this
counts
set
1
merge
other
ObservationCounter
ObservationCounter
const
new
ObservationCounter
for
const
of
this
counts
counts
set
Math
max
counts
get
0
for
const
of
counts
if
counts
has
counts
set
return
value
number
return
Array
from
this
counts
values
reduce
(a, b ) =>
0
class
AgentCircuitBreaker
private
0
private
lastFailure
Date
private
state
'CLOSED'
'OPEN'
'HALF_OPEN'
'CLOSED'
async
agent
Agent
action
() =>
Promise
Promise
if
this
state
'OPEN'
if
this
shouldAttemptReset
this
state
'HALF_OPEN'
else
throw
new
CircuitOpenError
id
try
const
await
action
this
onSuccess
return
catch
this
onFailure
throw
private
onSuccess
void
this
failures
0
this
state
'CLOSED'
private
onFailure
void
this
failures
this
lastFailure
new
Date
if
this
failures
FAILURE_THRESHOLD
this
state
'OPEN'