AI agent development decisions
Build vs Buy: buy the substrate, build the differentiator.
The 2026 answer is almost never 'all build' or 'all buy.' Implement Agentic's stance: buy the horizontal substrate (search, connectors, governance) and build the narrow vertical agents that encode your business logic — with MCP as the seam that keeps both sides swappable.
The six gates — apply in order
We walk every engagement through these gates during the Readiness Assessment.
- 01
Is the workflow horizontal and well-trodden?
Universal search across SaaS, generic chat-with-docs, basic meeting summarization → Buy turnkey (Glean, Microsoft 365 Copilot, Notion 3.0, Dust).
- 02
Is the workflow vertical and proprietary?
Solution-engineering, SOW logic, case-study assembly → Build composable (LangGraph + Claude Agent SDK + pgvector + Unstructured + Langfuse + MCP).
- 03
Is data residency or air-gap mandatory?
Build with self-hosted Airbyte + Unstructured + Langfuse + Qdrant or pgvector, single cloud account per engagement.
- 04
Multi-tenant client isolation?
Either single-tenant Glean per client, or a multi-tenant build with ironclad ACL enforcement at the index layer. Don't try multi-tenant on a SaaS not designed for it.
- 05
Do you have <2 ML engineers?
Buy. Small data-science teams burn out re-implementing RAG/connectors/governance. A basic enterprise RAG knowledge system costs $750K–$1M with 2–3 dedicated engineers per Gartner.
- 06
Is the differentiator workflow or intelligence?
If workflow → build orchestration but buy models, vector DBs, parsers. If intelligence (rare) → consider fine-tuning on stable behaviors only.
Five workflow patterns
For each pattern: a buy-first option, a build-first option, and Implement Agentic's recommendation.
| Workflow | Buy | Build | Implement Agentic recommends |
|---|---|---|---|
| AI-assisted sprint planning | Atlassian Rovo, Linear AI, Zenhub + Copilot, monday agents | LangGraph agent over Jira/Confluence with predictive velocity model | Build a thin LangGraph agent that pulls customer requirements + past tickets + capacity and proposes a sprint plan as a Jira draft. 60–70% productivity gains reported. |
| Contract / SOW drafting | Ironclad AI, Spellbook, Harvey (legal), Notion 3.0 Custom Agent | LangGraph with structured Requirements → Scope → Deliverables → Pricing → Risk → T&Cs flow over a clause library | Build. SOW logic IS the differentiator. LlamaParse/Reducto on past SOWs → clause library → LangGraph drafts → human reviewer finalizes. Never autonomously send. |
| Case study generation | Writer, Jasper, Notion 3.0 | Agent over the project's full lifecycle (transcripts → SOW → sprints → docs → feedback) | Build. The asset IS the closed-loop archive. Capture with Granola/Fireflies, consolidate in Notion, draft with a small Claude agent. |
| RFP & proposal writing | Loopio, Responsive, RFPIO | LangGraph agent on top of a tagged win/loss vector index | Hybrid. Buy the RFP Q&A database (Loopio); build a LangGraph agent that customizes win-themes per industry. 3.4× throughput, 19→27% win rate reported. |
| Customer requirements from meetings | Gong, Read.ai | Granola/Fireflies → MCP server → LangGraph agent that links 'customer wants X' to past patterns | Build. This is where the closed loop becomes most valuable — an agent that compares discovery calls to past delivered solutions in real time. |
Why hybrid wins
Time-to-value
Turnkey suites get connectors and search live in weeks; building these from scratch takes quarters.
Differentiation
Your competitive edge is your workflow logic — not the vector DB. Build only where it matters.
Portability
MCP keeps the buy-vs-build seam clean. Swap models, vendors or platforms without rewriting integrations.
Get your build-vs-buy verdict for AI agent development in two weeks.
Our Readiness Assessment delivers a workflow-by-workflow recommendation with ROI model and tool shortlist. Ready for MCP? We can scope that on the same call.