Already trying
Claude Managed Agents?
Here's when you'll outgrow it.
You got Claude Code working for yourself. Or you helped someone else on your team get it working — a non-technical colleague, your CEO, the ops lead who wanted to automate the grunt work. It works. The workflow is good. And now somebody is asking you to run it for the whole team. This is the part nobody talks about.
Claude Managed Agents is the obvious next thing to try. It's in the same tab. It promises managed sandboxes, execution tracing, and autonomous sessions. It takes a weekend to get a prototype running. A lot of teams are trying it right now, including some of our own customers. There's nothing wrong with doing that.
But there are four walls you're going to hit when the workflow gets real. The speed wall. The humans wall. The files wall. The learning wall. They're the same walls we built Nicia to cross. Here's what each one looks like — and here's what changes when you walk through them.
Credit where it's due
What Claude Managed Agents is great at
We send our own customers to CMA when it's the right fit. It earns that recommendation honestly:
- Fast prototyping inside the Claude Console for teams already committed to Anthropic
- Managed sandboxes, authentication, and tool execution handled out of the box
- Execution tracing integrated directly into the Claude developer tooling
- Strong default behavior on autonomous tasks that fit inside a single model's context
The four walls
What you'll hit when the workflow gets real
In the order most teams hit them. Same walls every team hits when a laptop workflow turns into a team workflow.
The speed wall
Agents running on a single big model take too long. Bulk tasks that should take 30 seconds take 15 minutes. Users give up.
What it looks like.The first time your colleague waits two minutes for an agent to classify an inbound email, they tab away. The second time, they don't open the tool. The third time, they ask if they can just do it manually. Speed isn't a polish item — it's the difference between a workflow your team uses and a workflow your team avoids.
Why CMA can't fix this structurally.CMA's autonomous sessions are designed to keep work inside one model for the whole loop. That's the architecture. Routing each step to a smaller, faster model would mean leaving the Claude loop, which is against the model vendor's incentives. The platform is doing exactly what it was built to do.
What changes in Nicia.Per-step model routing. Bulk classification on Haiku. Hard reasoning on Sonnet or Opus. Tool calls run as tool calls, not as model invocations. Sandboxes spin up fast. The work finishes in the time the work should take, and your team stays in flow.
The humans wall
A workflow requires approval from a specific person before something ships. Autonomous loops can't accommodate this cleanly.
What it looks like.Compliance has to sign off before the email goes out. Legal has to read the contract before it gets sent. The brand lead has to bless the copy. Every workflow that matters in the enterprise has a human in it somewhere — by name, with judgment, with accountability.
Why CMA can't fix this structurally.Autonomous-session primitives are built around the assumption that the model finishes the loop. Retrofitting "wait for a specific person to approve" touches the data model, the execution loop, the tracing layer, and the API surface. It's not a feature you bolt on. It's a different architecture.
What changes in Nicia.Handoffs are first-class. Tasks declare which named people are allowed to respond. Approval gates carry a rubric so the reviewer knows exactly what to check. Every human decision becomes part of the run history, and the next run learns from it.
The files wall
The agent's output is a file or document that needs lineage, versions, and diffs. Chat transcripts don't capture what actually matters.
What it looks like.The agent produces a brief, a contract draft, a brand voice update, a pricing sheet. Two weeks later, somebody asks "why did this change?" The chat transcript doesn't say. The artifact has no lineage. The brand lead can't trace which run produced which line. Knowledge is in motion but no one can hold it.
Why CMA can't fix this structurally.CMA's primitive is the session. Sessions produce transcripts. Transcripts are great for debugging the model and lousy for versioning the work. Files, diffs, and adoption decisions are a different data model entirely.
What changes in Nicia.Artifacts are first-class. Every file an agent produces or a user uploads has lineage, versions, and diffs across runs. Sources sync from Google Docs and Notion. When a source changes, you see the diff and decide when your agents adopt it.
The learning wall
The agent works, but nothing gets better across runs. Every new team member starts from zero. Institutional knowledge doesn't accumulate.
What it looks like.The agent does fine work today and the same fine work in a year. When a senior reviewer corrects an output, that correction lives in their head. New hires repeat the same mistakes. The thousand tiny judgments that make the workflow good never accumulate anywhere a future run can use them.
Why CMA can't fix this structurally.Model self-evaluation improves a session from inside the session. It doesn't write back to the prompts, the rubrics, the context, or the human gates that govern the next session. The cold-start data that would teach the platform what "better" means for your operation isn't in the model vendor's hands.
What changes in Nicia.Every run is graded against your Goals. Every correction becomes a ChangeSet that proposes a specific, reviewable improvement to the prompt, the context, the rubric, or the skill. You approve. The next version scores higher. Your operation compounds.
Side by side
The same workflow, four ways
Build it from scratch. Run it on a laptop. Hand it to the model vendor's loop. Or run it on Nicia.
| Build from scratch | Personal use | Managed by model vendor(Claude Managed Agents, etc.) | Nicia | |
|---|---|---|---|---|
| Starting point | New code and orchestration logic | Files on one laptop | Claude-native SDK, single-vendor surface | Your existing skills, prompts, scripts — imported as-is |
| Execution | Your infra, your problem to operate | Your laptop. One user, one credential. | Managed sandboxes, Claude only | Managed sandboxes, any model, fast cold-start by default |
| Speed | Whatever your code's latency adds up to | Whatever the laptop runs | Long-running autonomous sessions; one big model per loop | Right-sized models per step. Bulk on tiny, hard reasoning on big. Finishes fast. |
| Coordination | Your code, your retries, your queue | Single agent | Claude multi-agent (research preview) | Prompt-driven task graphs, emergent at runtime |
| Context | Build it yourself: vector store, retrieval, prompts | Whatever you paste into the terminal | Model memory + tool calls inside the loop | Distilled sources, artifacts, prior decisions carried into every run — versioned and diffable |
| Evaluation | Build it yourself, after the fact | Informal — eyeball the output | Self-evaluation loop, scoped to the session | Goals, evaluations, leaderboards across versions |
| Oversight | Your logging, your audit story | Watch the terminal | Execution tracing inside the Claude tooling | Audit trail, budgets, approval gates with named responders |
| Improvement | Manual iteration, ad hoc | Ad hoc — you tweak the prompt | Autonomous self-evaluation loop, no human in the path | Evaluation-driven, governed, human-approved ChangeSets — every approval teaches the next run |
| Scaling | Your problem to solve | Doesn't. It's one laptop. | Within the Claude ecosystem; one vendor's loop | Reusable across teams, models, and workflows |
Build from scratch: CrewAI, AutoGen, LangGraph, Temporal+LLM • Personal use: Claude Code, Aider, Cursor • Managed by model vendor: Claude Managed Agents
Migration
Migration is easy
Nicia treats Claude Code as a personal tool we scale up — not a competitor. Your skills, prompts, and keys come with you.
- Claude Code skills import directly via the Agent Skills standard
- Runs on your existing API keys (BYOK via AI Gateway)
- No rewrite of prompts or agent logic required
- Import your existing artifacts and sync your existing sources