Custom agent engineering, when the framework stops fitting
Frameworks get you 80% of the way fast. We build the remaining 20% — bespoke orchestration, typed tools, and memory tuned to your systems, latency, and compliance constraints — without locking you to any vendor.
- Bespoke orchestration loops
- Typed, audited tool layer
- Memory & retrieval tuned to your data
- Open interfaces, your repos
A spectrum, not a rewrite
Customization is a dial, not a switch.
Most agent projects live happily on a framework. LangChain, CrewAi, and AutoGen give you battle-tested abstractions for prompting, tool calls, and multi-agent coordination, and for the majority of workflows that's exactly the right altitude to build at.
But every real deployment hits edges the defaults didn't anticipate: a control flow that isn't a simple loop, a tool that needs transactional guarantees, a cost ceiling that a generic agent blows through, or a compliance rule that has to be enforced in code rather than a prompt.
Custom engineering is how we meet those edges. We keep the proven pieces and replace only the layer that's fighting you — usually the orchestration loop, the tool contracts, or the memory strategy — so you get control exactly where it pays off and convention everywhere else.
Where bespoke code earns its place
Each layer is an opt-in. We change only the ones a framework can't serve well.
Orchestration loop
A purpose-built control loop — branching, retries, parallelism, and stop conditions written for your task, not a generic agent executor.
Typed tool layer
Hand-written tool contracts with strict schemas, idempotency, and error semantics so every action against your systems is predictable.
Memory & retrieval
Retrieval and state tuned to your corpus — chunking, ranking, and context budgets shaped by what the agent actually needs.
Model routing
Route each step to the right model — a small model for triage, a frontier model for judgment — across providers, behind one interface.
Policy gates
Approval thresholds, allow-lists, and spend caps enforced in code, where a prompt can't be talked around.
Tracing & evals
Recorded decision traces and a regression suite of real tasks, so behavior is observable and changes stay safe.
From framework to fitted runtime
We earn each line of custom code instead of starting there.
Prototype
Stand the workflow up on a framework first to learn the real shape of the problem fast.
Profile
Measure cost, latency, accuracy, and failure modes to find exactly where defaults break.
Replace
Swap only the fighting layer — loop, tools, or memory — for tested custom code.
Harden
Add policy gates, traces, and evals, then hand over repos, tests, and runbooks.
Custom, but yours to keep
Bespoke does not mean proprietary. Every custom agent we build sits on open interfaces — standard model SDKs, swappable vector stores, and an orchestration core you can read in an afternoon — committed to your repositories under your control.
The point of customization is to remove constraints, not add a new one named Automatic.co. You can change model providers, lift the runtime onto different infrastructure, or take maintenance fully in-house without a migration project.
- Open model & tool interfaces
- Code, tests, and runbooks in your repos
- Swap providers or orchestrators freely
Framework defaults vs. custom engineering
Two valid choices — the trick is knowing which layer needs which.
| Stay on the framework | Build it custom | |
|---|---|---|
| Best for | Common workflows, fast starts | Edge cases the defaults fight |
| Control flow | Built-in agent executor | Purpose-built loop & stop logic |
| Cost & latency | Good enough out of the box | Tuned routing and context budgets |
| Guardrails | Prompt- and config-level | Enforced in code, at the gate |
| Maintenance | Framework upgrade cadence | Your repo, your tests, your pace |
Frequently asked questions
When do you go custom instead of using a framework?
When the framework abstractions cost more than they save — unusual control flow, strict latency or cost budgets, hard compliance boundaries, or tool surfaces that don't map cleanly onto LangChain or CrewAI conventions. We start with a framework and peel away to custom code only where it earns its keep.
Does customization mean you write everything from scratch?
No. We keep proven building blocks — model SDKs, a durable execution engine like Temporal, vetted vector stores — and write custom glue: the orchestration loop, the typed tool layer, and the policy gates. You get control where it matters without re-inventing infrastructure.
Won't custom code lock us in to Automatic.co?
It's the opposite. We hand over readable code, tests, and runbooks in your repos, against open model and tool interfaces. Swap providers, change orchestrators, or take it fully in-house — nothing here depends on us staying in the loop.
How do you keep a custom agent maintainable?
Typed tool contracts, a thin orchestration core, recorded decision traces, and a regression suite of real tasks. The agent's behavior is testable and observable, so changes are safe and the next engineer can reason about it.
Hitting the edge of what a framework can do?
Bring the workflow that's fighting your stack. We'll show you which layer to customize — and which to leave alone.