acme
Get a demo

AI agent orchestration that keeps many agents in step

A single agent handling a single task is usually reliable. Problems appear when several have to cooperate: work slows down, hand-offs get muddled and costs creep up. Acme coordinates the whole group so they operate like a team that knows its plays.

Watch coordinated agents at work in production

Describe the job, and Acme builds the agent

AI agent orchestration is the coordination layer that assigns each step to an agent, sets the sequence and hands over the context each step needs. Few people want to draw rigid flowcharts or untangle nested branches; they would rather say what outcome they want. In Acme you write the task in ordinary language, and Acme splits it into steps, adds the structure and assembles the agent.

  1. Following the thread when a user switches subjects
  2. Holding on to details already provided
  3. Asking follow-up questions only if needed
  4. Adjusting the plan as the conversation evolves

Instead of marching people through a fixed script, they get to talk naturally. You explain the work, and Acme returns an agent that adapts, draws on your data and is ready to go.

Coordinate workflows from end to end

Without a coordination layer, multi-agent setups fall apart fast. Steps fire in the wrong order, state disappears, failures hide in log files and no one can say which agent broke or why. The Acme orchestration engine gives every workflow a defined shape and a durable way to run, and it handles the hard engineering on your behalf:

  1. Declared, not codedDescribe branches, loops, retries, escalations and sign-off steps without wiring agents together by hand.
  2. Saved state at every stepProgress is stored after each step, so a workflow can pause, pick up later or recover from an error with its context intact. No restarts from scratch.
  3. Side-by-side or one-after-another runsAgents run together when they can and in a fixed order when they must, and Acme tracks every dependency.
  4. Guarded tool callsEach call an agent makes is checked against permissions and logged, so actions are traceable.
  5. Clear visibilityTrace views, inputs, outputs and per-step detail turn debugging into a quick task rather than a hunt.

Behind the scenes, Acme keeps every run predictable and safe for production, so your agents act as one coordinated unit rather than a loose set of bots.

Give agents safe access to company data

Agents can reach the information they need through Acme without opening up your systems or adding new attack surface. Each connection respects permissions, is logged and stays tightly scoped.

  1. Models never touch the credentialsAPI keys, tokens and database logins stay out of the agent's reach; Acme runs actions in a protected layer.
  2. The access rules you already haveIdentity policies, groups and app-level permissions are honored automatically, whichever ones your company relies on.
  3. Starts out read-onlyWrites are limited, approved and recorded, so nothing changes without your say-so.
  4. Automatic masking of sensitive fieldsPrivate values are masked or hashed, keeping logs tidy with no extra engineering effort.
  5. A safe route into every system you runDatabases, in-house APIs and SaaS tools all connect through Acme, with no custom scripts or fragile glue code.
  6. Every action on recordTool calls, decisions and each piece of data touched are logged automatically for audits.
  7. Data that's always freshAgents read from live company systems, never from stale copies or ad hoc fetchers.

Keep improving after launch

Launch day is the beginning, not the end. Acme lets you keep refining how agents reason, what they can draw on and which actions they may take, without disrupting production or your compliance posture.

  1. Move quickly, undo easilyEach change (an extra step, another model, a reworded prompt, a new tool) is saved as its own version. Try it, compare how it behaves and roll it back instantly when it falls short with real users.
  2. Decide from traces, not hunchesAcme records complete agent traces: the tools that were called, the data that was read, where a conversation forked and where it broke. You can pinpoint the step that was slow, drifted or ran into a permission block, and fix that one piece instead of reworking everything.
  3. Turn user feedback into fixesThumbs up or down, hand-offs to people, edits to drafts and approvals on sensitive actions all count as signals. Acme surfaces the patterns, so you can sharpen instructions, add guardrails or break an overloaded agent into narrower specialists based on real usage.
  4. Tune each prompt, tool and model separatelyTry a different model, rewrite an instruction or retire a shaky internal API in favor of a safer tool, all without redrawing the entire flow. Reasoning, orchestration and execution stay loosely connected in Acme, so each layer can be adjusted independently.
  5. Fix the source data, not only the agentWhen an agent gets it wrong, the culprit is often the source material rather than the model. Acme shows exactly which documents an answer used, so owners can update stale pages or fill gaps, and every agent relying on them improves together.
  6. Reuse what works across all your agentsDiscovered a smarter way to triage, review or hand off work? Share it with other agents and teams so nobody has to learn the same lesson again from scratch.

In Acme, getting better is never a risky overhaul; it's the steady way agents become quicker, safer and more capable every week.

Picking an AI agent builder: the features that count

Comparing agent builders? These are the checks that make a difference.

  1. Action, not just chatAgents should be able to make choices and carry out real actions, not only produce text.
  2. Orchestration includedExpect branches, loops, retries and human sign-off. A tool that only handles straight-line flows won't last in production.
  3. Permission-aware data accessRole-based controls, automatic redaction and minimal privileges from the start. If your data isn't safe, no other feature counts.
  4. Every step visible and loggedEach action an agent takes should be visible, with its tools and reasons. Audit trails are a must.
  5. Visual building that still goes deepBuild and adjust agents on a canvas, while still adding logic, conditions and the best-suited model for each step.
  6. Fits the stack you already runNative connectors, open APIs and the freedom to bring the models you prefer. Steer clear of vendor lock-in.
  7. Versioning and safe rollbackVersions, tests and rollbacks happen automatically, so agents can improve without disrupting live workflows.

Inside Acme's enterprise AI

Ask

Start with a question in Acme's search bar. A file, a chat thread or a detail hidden in an old ticket: Acme looks through every connected tool at the same time to find it.

Interpret

Models and the company knowledge graph figure out the intent behind your words, your role and the sources worth trusting, so replies use the terms and context your team uses.

Answer

Acme writes a concise reply from the strongest sources, cites each one and proposes what to do next, so you can move on immediately or open the originals for detail.

Tailor

Because the graph connects people, projects and documents, results shift with your role, what you worked on recently and the colleagues you team up with most often.

Protect

Answers follow the access rules of each source app, stay current as content changes and keep sensitive material where it should be, with controls for admins built in.

FAQs

It's the conductor for a mix of AI agents, tools and people: it picks which agent takes each step, carries context from one to the next, handles retries and approvals, and keeps a record of everything.

Often it can, and that's what we suggest. Orchestration earns its keep when you need specialists, parallel work or a firm boundary between kinds of tasks, such as a legal review running apart from a finance review.

Only when it's built poorly. Long chains of agents in sequence can add delay and spend, but a well-structured setup that runs work in parallel, caps tool calls and refines in stages often beats one overloaded agent on both.

Start small for the best results: one triage agent, a couple of specialists and tracing switched on for every run. Expand only once you've seen where specialization or parallel work actually pays off.