Raltic field guide
Build vs buy AI agent orchestration: choose the layer first
Teams often ask whether to build or buy before agreeing on what they need. A runtime, an automation graph, a reviewer interface, and a shared team record are separate layers with different ownership costs.
Move from the definition to the operating decision without losing the thread.
Direct answer
Build AI agent orchestration when the agent behavior is part of your product, you need custom state and recovery, and your team can own runtime, evaluation, security, and reviewer experience. Buy when the workflow is an internal operating process and the product already supplies the shared context, permissions, review, and history you need. Many teams should combine both: build the domain-specific agent or graph, then buy the surrounding collaboration, observability, or governance layer.
Separate the six layers before estimating anything
Most build-versus-buy debates combine components that can be chosen independently.
The model decides and generates. A framework structures tools and agent loops. A runtime handles state, queues, persistence, and recovery. An automation layer connects systems and triggers. A team surface carries briefs, tasks, evidence, and review. Governance covers identity, policy, evaluation, and audit requirements.
Write down which layers create product differentiation and which are operating infrastructure. Building a custom research planner may be sensible. Building another login system, notification service, reviewer inbox, and activity history often is not.
- 01Model and tool loop
- 02Framework or graph
- 03Runtime and durable state
- 04Integrations and triggers
- 05Team workflow and review
- 06Governance and evaluation
Build when orchestration behavior is part of the product
Custom code is justified when the workflow itself creates defensible product value or requires unusual control.
A customer-facing agent may need domain-specific state, deterministic branches, custom recovery, latency targets, and tightly controlled tool execution. Frameworks such as LangGraph and CrewAI exist for this job. They give developers primitives without deciding the final product experience.
The build estimate must include more than the first successful run. Add deployment, state migrations, retries, observability, evaluation datasets, access control, red-team cases, support, and the UI where a person handles exceptions.
- 01Agent behavior differentiates the product
- 02State and recovery are domain-specific
- 03Latency or deployment constraints are unusual
- 04The team owns long-term runtime operations
- 05A custom reviewer experience is funded
Buy when the workflow is important but not proprietary
A team should buy the surrounding operating layer when speed, adoption, and continuity matter more than custom runtime logic.
Internal launch review, research synthesis, customer-risk analysis, and routine handoffs rarely need a bespoke orchestration engine on day one. They need a clear brief, available evidence, bounded agent access, visible progress, a reviewer, and a record that survives the meeting.
Buying also makes sense when non-developers own the workflow. If every change to a review step needs a framework engineer, the technical architecture has captured a business process that should remain adaptable.
- 01The process is common across teams
- 02A usable room or workflow already exists
- 03Non-developers own acceptance
- 04Time to first reviewed artifact matters
- 05Custom runtime control adds little value
The practical answer is often hybrid
Build the narrow intelligence or runtime that matters, and buy the undifferentiated operating surfaces around it.
A team might build a LangGraph application, deploy it on a cloud agent platform, use n8n for deterministic integrations, and keep human review in an existing work system. Another team may connect Claude Code and Codex through Raltic and avoid building orchestration code altogether. Both are coherent if ownership boundaries are explicit.
Before committing, draw the data and control path. Name who owns each credential, where state persists, what the reviewer sees, which system authorizes the action, and how the team recovers when one layer fails. The diagram usually settles the argument faster than a long feature spreadsheet.
- 01Build the differentiated agent logic
- 02Buy commodity workflow surfaces
- 03Keep authorization in the destination system
- 04Connect layers through explicit contracts
- 05Test failure across every boundary
Primary sources
Product and practice claims in this guide are grounded in current first-party or standards sources. Raltic product claims are limited to shipped behavior described on this site.
FAQ