Multi-Agent Systems
Specialist Agents, Working Under Orchestration.
Some problems are too broad for one agent. Bralak builds systems where specialised agents handle their own domain under an orchestrator that manages context, sequencing and control.
What this covers
- Orchestration
- Specialist agents
- Context management
- Shared memory
- Integrates with
- 1 system types
- Built on
- LangGraph · MCP
What it is
Multi-Agent Systems, in plain language
A single agent given too many tools and too broad a goal degrades. It loses track of context, picks the wrong tool, and becomes difficult to evaluate because every failure has many possible causes.
Multi-agent architecture addresses this by narrowing scope. Each agent has one domain, a small tool set and a clear success condition. An orchestrator decomposes the request, routes work, manages shared context and assembles the result.
This is an architectural decision with real costs — more moving parts, more latency, more to observe. It is worth it when a task genuinely spans domains. It is not worth it as a default.
Capabilities
What the system does
Orchestration
A supervising layer that decomposes goals, routes work and decides when the task is done
Specialist agents
Narrow scope, small tool sets, individually evaluable
Context management
Explicit control over what each agent sees, preventing context bloat and leakage
Shared memory
A common state store with defined read and write boundaries per agent
Governance
Per-agent permissions, action classification and escalation policy
Observability
Distributed tracing across agent handoffs, so a failure is attributable to a step
How it works
The architecture, not the pitch
Orchestrator decomposing a request across research, sales and support agents, each with its own tools and knowledge, converging on one action.
- InputRequest
- ReasoningOrchestratorDecomposes and routes
- RetrievalSpecialist agentsResearch · sales · support
- SystemShared memoryDefined read/write boundaries
- DecisionAssemblyOne coherent result
- ActionWork completed
- Human escalationEscalationAny unresolved part goes to a person
The hard parts
The costs you take on by adding a second agent
This architecture has real overhead. It is worth paying for a specific reason, and it is the wrong default.
- 01
Context is passed deliberately or it bloats
Handing every agent the whole conversation is the easy implementation and the expensive one: cost climbs, latency climbs, precision falls. Each agent receives a defined slice — which also means one agent’s context cannot leak into another’s answer.
- 02
Termination is a genuine design problem
Agents that can call one another can loop. Every system gets step budgets, cycle detection and a defined terminal state, because finishing is not a property that emerges on its own.
- 03
Failure has to be attributable to one agent
When a five-agent run produces a wrong answer, the only useful question is which step was wrong. Each agent is evaluable against its own cases and traces span the handoffs; without that, debugging is re-running the whole chain and guessing.
- 04
Handoff is a schema, not a conversation
Agents exchange structured results with defined fields rather than prose the next agent has to re-interpret. Prose handoffs compound small misreadings into a confidently wrong final answer.
- 05
Latency and cost accumulate per hop
Each agent is at least one model call, usually several. A four-agent chain is not four times better; it is reliably four times slower. The split has to buy something specific — a genuinely different tool set, or a different evaluation standard — or it should be one agent.
- 06
Shared state needs a single owner
Once several agents read and write the same working memory, stale reads and concurrent updates become possible. One agent owns each piece of state, or writes go through the orchestrator.
Use cases
From trigger to business outcome
Every row reads the same way, because every system does: something happens, the model interprets it, an action lands in a real system, and the business result follows.
| Use case | Trigger | AI reasoning | Action | Result |
|---|---|---|---|---|
| Account research and outreach | A target account is added | Research agent gathers public context; sales agent drafts positioning against your offering | Produces a briefed outreach draft with sources for a human to approve | Research stops being the reason outreach doesn’t happen |
| Complex support resolution | A ticket spans billing, technical and account questions | Orchestrator splits it; each specialist retrieves within its own domain | Assembles one coherent response, escalating any part that is unresolved | Multi-domain tickets stop bouncing between queues |
| Document processing at scale | A batch of mixed documents arrives | Classifier routes by type; specialist extractors handle each format | Writes structured output with per-document confidence | One pipeline handles document variety without a rule per format |
| Operational monitoring | A threshold is breached | Monitoring agent diagnoses; remediation agent identifies the documented fix | Applies it, or raises an incident with the diagnosis attached | Known issues stop waiting for someone to notice |
Account research and outreach
- Trigger
- A target account is added
- AI reasoning
- Research agent gathers public context; sales agent drafts positioning against your offering
- Action
- Produces a briefed outreach draft with sources for a human to approve
- Result
- Research stops being the reason outreach doesn’t happen
Complex support resolution
- Trigger
- A ticket spans billing, technical and account questions
- AI reasoning
- Orchestrator splits it; each specialist retrieves within its own domain
- Action
- Assembles one coherent response, escalating any part that is unresolved
- Result
- Multi-domain tickets stop bouncing between queues
Document processing at scale
- Trigger
- A batch of mixed documents arrives
- AI reasoning
- Classifier routes by type; specialist extractors handle each format
- Action
- Writes structured output with per-document confidence
- Result
- One pipeline handles document variety without a rule per format
Operational monitoring
- Trigger
- A threshold is breached
- AI reasoning
- Monitoring agent diagnoses; remediation agent identifies the documented fix
- Action
- Applies it, or raises an incident with the diagnosis attached
- Result
- Known issues stop waiting for someone to notice
Integrations
Built around the systems you already run
- As per the constituent agents — CRM, ticketing, data warehouse, internal APIs
Technology
What this is built with
- LangGraph
- MCP
- OpenAI
- Anthropic
- Python
- FastAPI
- PostgreSQL
- Redis
- OpenTelemetry
How we work
Five phases, each with a decision point
- InputDiscover
- ReasoningArchitect
- DecisionPrototype
- SystemProduction
- ActionOptimize
FAQ
Questions we are actually asked
When do we actually need multiple agents?
When a task genuinely spans domains with different tools and different knowledge. If one agent with a focused tool set can do the job, use one agent — multi-agent adds latency, cost and failure surface.
How do you stop agents talking in circles?
Explicit orchestration rather than free-form negotiation. The supervisor decides sequencing and termination; agents do not decide among themselves when the work is finished. Step limits and budgets apply per run.
How do you debug it?
Distributed tracing across handoffs. Each agent’s inputs, tool calls and outputs are recorded as spans, so a wrong outcome is traceable to the step that caused it.
Isn’t this slower?
Usually, yes — more steps take more time. Where latency matters we parallelise independent agents and cache retrieval. Where it does not, the accuracy gain is worth the seconds.
Can we start with one agent and grow into this?
That is the recommended path. Build one agent, find where its scope strains, then split along that seam. Starting multi-agent usually means guessing the boundaries wrong.
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