Industry solution

AI & Automation

Put AI agents to work with approvals, tests and costs you can stand behind

Grounded knowledge, scoped tool access, human approval, pre-release testing, cost per outcome and trained supervisors, built to work as one.

Products
8
Challenges
6
Concepts
18
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A team lead reviews an agent action approval on a laptop while colleagues work nearby in an operations room.
Botlit(opens Botlit in a new tab)FluidGrids(opens FluidGrids in a new tab)VibeControls(opens VibeControls in a new tab)BigConsole(opens BigConsole in a new tab)SemanticFed(opens SemanticFed in a new tab)AdapterCloud(opens AdapterCloud in a new tab)BuildMyIQ(opens BuildMyIQ in a new tab)TechnoSpam(opens TechnoSpam in a new tab)

The problem

Why running AI agents in production is harder than the demo

Agents reach production faster than the controls around them, so stale knowledge, broad tool access, unreviewed actions, untested model changes and unexplained AI bills surface only when a customer or finance finds them.

Getting an agent to work in a demo takes an afternoon. Running it in production is an operating job. The agent answers customers from documents that change every month, calls tools that move money or change accounts, and runs on models whose behavior shifts with each version. Someone has to own what the agent knows, what it may do, who signs off when the stakes rise, and how a change is checked before customers see it.

In most companies those answers are scattered. Agents are built by different teams on different platforms, automations live in workflow tools with approvals handled in chat, coding assistants run on engineers' laptops on their own AI accounts, and AI spend arrives as separate bills from model providers, cloud accounts and tool subscriptions. When something goes wrong, incident review means rebuilding a conversation from logs and screenshots.

The people side moves slowest. Support leads become agent supervisors, controllers become approvers, and engineers are expected to write evaluations as well as code, usually without training. The few people trusted to approve or evaluate become the bottleneck, and adoption stalls at the point where the organization can no longer supervise what it has automated.

Who this is for

  • Head of AI or AI platform lead

    Agents that answer from owned, current knowledge, with every new version tested before release and every run on record.

  • Automation center of excellence lead

    Automations that stay switched on because risky actions wait for a certified approver and failed runs can be diagnosed.

  • CTO or VP Engineering

    Coding agents that work from approved plans, with model keys and autonomy settings visible and set from one console.

  • CFO or FinOps lead

    Model, compute and AI tool spend tied to teams and outcomes, such as the cost of one resolved support case.

  • AI risk, security or governance lead

    A clear record of which agent can use which tool and data, under whose permissions, and what it actually did.

  • Head of learning or enablement

    Role-based training that turns support leads and engineers into certified agent supervisors, reviewers and builders.

A day in the life

The story behind the solution

One week running agents in production at an AI-native software company

Adaeze Okafor runs AI operations at Marlowby, a fictional 280-person software company whose expense-management app is supported by AI agents. Agents handle first-line support and billing questions, automations issue routine credits, and engineers use coding agents for most small changes. This is one week, and every moment in it is ordinary for a company that runs on agents.

  1. Monday, 8:50

    A refund window that changed last month

    Adaeze's support lead, Tomás, forwards eleven overnight transcripts. The support agent told each customer they had 30 days to dispute a charge. The policy moved to 14 days on the first of the month, but the old policy document still sits in the knowledge base next to the new one, and the agent cited both. One customer was also quoted a receipt limit from last year's pricing page. Tomás has one question: which documents does the agent actually trust?

    How this is solved: Agents answering from last month's policy

  2. Monday, 14:20

    A tool nobody meant to grant

    An engineer connected a new set of tools so the billing agent could look up invoices. The same set also lets it issue credits and close accounts. Hana, the security reviewer, asks for a simple list: which agents can call which tools, read which data, and under whose permissions. Putting it together means three admin screens, a chat thread and one engineer's memory.

    How this is solved: Agents holding more access than anyone agreed to

  3. Tuesday, 10:05

    Forty credits before breakfast

    The billing automation issued forty goodwill credits overnight, exactly as built. Thirty-nine were small and fair. One was an $1,800 credit to an account already in a payment dispute. The workflow had no step where a person reviews credits above a threshold, and the finance controller found it in the ledger. The first suggestion in the room is to switch the automation off, which would send hundreds of routine cases back to the billing team.

    How this is solved: Automations that act before a person has looked

  4. Wednesday, 16:30

    The cheaper model that misrouted tickets

    Last Thursday the triage agent moved to a cheaper model. Since then, billing tickets have been landing with technical support, and one escalation reached a large customer's finance director. In the incident review, nobody can say which instructions version was live, which passages the agent used, or whether anyone tested the change. There is no saved set of questions to rerun, so the review becomes a hunt through logs.

    How this is solved: Agent changes shipped untested, incidents nobody can replay

  5. Thursday, 11:00

    An AI bill with no owner

    Finance asks why AI spend rose again. The answer is spread across three model providers' invoices, a cloud bill that includes a graphics processor cluster that sat idle all weekend, and coding assistants' AI accounts paid on engineers' own cards. The CFO wants one number Adaeze cannot produce: what a resolved support case costs with the agent, and whether the cheaper triage model saved more than its escalations cost.

    How this is solved: Model, compute and tool bills nobody can tie to an outcome

  6. Friday, 15:10

    Three approvers for two hundred decisions

    Adaeze sketches the approval step the billing automation needs and hits a wall: only three people are trusted to approve agent actions, and one is on leave next week. The support leads now supervising agents were never shown how to judge an agent's answer or reject an action well, and only two engineers know how to write evaluation cases. She needs a bench of trained reviewers, not a week of slide decks.

    How this is solved: Teams asked to supervise agents nobody trained them for

None of this is unusual for a company that runs on agents. What changes with connected products is that each agent's knowledge, permissions, approvals, tests, costs and supervisors are designed to connect across the products under one audit trail, so Adaeze's team can widen what agents do without losing track of who approved what, and why.

Challenges and how they are solved

6 problems, several products, one connected answer

Each challenge shows the problem as it happens, how the products are designed to hand work to each other, and the concepts that illustrate it. Share any challenge on its own.

Answers are in plain business terms. Choose Technical, or open any technical detail, to see how it works under the hood.

Challenge 1 of 6

Agents answering from last month's policy

Botlit(opens Botlit in a new tab)SemanticFed(opens SemanticFed in a new tab)FluidGrids(opens FluidGrids in a new tab)

The problem

A customer asks the support agent how long they have to dispute a charge. The knowledge base holds both the current 14-day policy and the superseded 30-day version, and the agent quotes the old one with a confident citation. Plan limits were pasted into a help article a year ago and never updated, so figures quoted to customers disagree with what billing actually enforces. No document behind the agent has a named owner or a review date, and the first sign of a stale source is a complaint or a refund the company never meant to offer.

What it costs

Customers receive commitments the company never made, support spends hours correcting them, and trust in the agent falls just as leaders want to widen its scope.

How the products work together

Botlit is designed to answer customers from approved documents, show which document each answer came from, and say so when it has no answer. Every document gets an owner and a review date, and old versions still in use are flagged. FluidGrids can remind owners when a review is due. SemanticFed supplies figures that change, such as seats in use, from one agreed definition.

How it works — technical detail

Technical detail

Botlit holds each agent's knowledge bases with their documents and index status, and its agents are designed to answer with numbered citations and say plainly when nothing matches. A knowledge freshness review is designed to give every source, such as the plan limits article, an owner and a review date, flag superseded or conflicting passages, and show which answers cited them. A scheduled FluidGrids workflow can remind owners when a review falls due and, once an owner retires or replaces a document, is designed to ask Botlit to rebuild the index. Figures that change often, such as seats in use or receipts processed this month, are designed to come from SemanticFed metrics defined once, with access rules applied, reached through SemanticFed's MCP server (in progress), so the agent quotes the live figure instead of a number copied into an article.

The outcome it is designed for

Every answer points to a current, owned source, stale documents are caught at review time instead of by customers, and changing figures come from one agreed definition.

The concepts behind it

Challenge 2 of 6

Agents holding more access than anyone agreed to

Botlit(opens Botlit in a new tab)SemanticFed(opens SemanticFed in a new tab)VibeControls(opens VibeControls in a new tab)

The problem

An engineer connects a new set of tools so the billing agent can look up invoices. The same set also lets it issue credits and close accounts, nothing records that the agent can now use them, and an instruction hidden in a customer email could steer the agent into calling them (prompt injection). Coding assistants run on engineers' laptops using AI accounts set up in local files, some allowed to run commands without asking. When a security reviewer asks which agents can call which tools, read which data and act under whose permissions, the answer is spread across three admin screens, a chat thread and one engineer's memory.

What it costs

One misread instruction could move money or delete records, access reviews take days, and every new tool connection becomes a reason for security to say no.

How the products work together

SemanticFed is designed to supply the billing agent's data tools, hold the agent to the same data rules as staff and keep a record of each request. Botlit lists every tool each agent can use and marks the risky ones, such as issuing a credit. VibeControls shows which AI keys each engineer's coding assistant uses on each machine and sets how far those assistants may act without asking.

How it works — technical detail

Technical detail

In this design, the billing agent's data tools come from SemanticFed's MCP server (in progress), registered in Botlit like any other tool server, so they appear in Botlit's tool list beside the agents that use them, with a risk level for each tool. Botlit's tool and budget policies are designed to set least-privilege tool scopes per agent and cap what a high-risk tool may do, with runtime enforcement on the roadmap; step-by-step tracing of tool calls is designed to follow once tools run inside the agent loop. Each call the agent makes is designed to reach SemanticFed under the agent's own identity, where the row, column and entity policies that apply to people apply too, and the session is recorded, including blocked calls. The reviewer reads Botlit's tool list and SemanticFed's session record side by side. VibeControls shows the same for coding agents: which provider keys sit on each agent machine and how far each may act without asking.

The outcome it is designed for

Every agent's tools, data scope and acting identity can be reviewed in one pass, high-risk tools are visible before first use, and access reviews start from records instead of interviews.

The concepts behind it

Challenge 3 of 6

Automations that act before a person has looked

FluidGrids(opens FluidGrids in a new tab)Botlit(opens Botlit in a new tab)BuildMyIQ(opens BuildMyIQ in a new tab)

The problem

The billing agent reads a complaint, decides a goodwill credit is fair and calls an automation that issues it. Overnight it issues forty. Most are small and correct; one is a large credit to an account already in a payment dispute. The workflow had no step where a person reviews credits above a threshold, and approvals for other automations happen in chat threads nobody can find later. When something goes wrong, the only safe move anyone knows is to switch the automation off and return to a manual queue.

What it costs

Money and account changes go out unchecked, controllers lose confidence in automation, and switching it off sends hundreds of routine cases back to people.

How the products work together

When a Botlit agent decides a customer should get a credit, it hands the request to FluidGrids with the conversation attached. Small routine credits are designed to go straight through; large or unusual ones wait for a named approver, who decides from a laptop or phone. BuildMyIQ supplies the list of trained approvers, so only certified staff receive these decisions.

How it works — technical detail

Technical detail

A Botlit agent that decides an action is needed triggers a FluidGrids workflow, passing the conversation and the passages it cited. FluidGrids' approval queue is designed to apply thresholds per action type: small, low-risk credits run straight through, while larger amounts, disputed accounts or first-time actions pause the run for one or two named approvers. Each approval pool is designed to be set from BuildMyIQ's current list of certified billing approvers, so a decision only reaches people trained for it, and the queue warns when a pool falls below its minimum. The approver is designed to see the agent's reasoning, the cited policy and the account's recent history, and to decide from the queue or from the run view on a phone; the run then resumes or stops, with the decision kept on the run record.

The outcome it is designed for

Routine actions stay automatic, risky ones wait for a named person with the evidence in front of them, and every decision is kept with the run it belongs to.

The concepts behind it

Challenge 4 of 6

Agent changes shipped untested, incidents nobody can replay

Botlit(opens Botlit in a new tab)FluidGrids(opens FluidGrids in a new tab)VibeControls(opens VibeControls in a new tab)

The problem

To save money, the triage agent is switched to a cheaper model on a Thursday afternoon. Nobody runs the questions that matter against it first, because there is no agreed set of test questions with known right answers. By Monday, billing tickets are being routed to technical support and one escalation has reached a large customer's finance director. The incident review stalls: which instructions version was live, which model answered, which passages were used and which automation ran next are scattered across logs, and nobody is sure a rollback is safe.

What it costs

Regressions reach customers before anyone notices, incident reviews turn into log archaeology, and teams become wary of changing models or instructions at all.

How the products work together

Botlit is designed to flag agents whose results slip after a change, such as a switch to a cheaper AI model, and suggest rolling back. The reviewer follows the agent's answer into the FluidGrids automation it started, which explains any failed step. An engineer fixes the agent in VibeControls under an approved plan, and Botlit is designed to test the new version against saved questions before it goes live.

How it works — technical detail

Technical detail

Botlit records every run with its agent, status, tokens, cost and model; its operations findings are designed to flag a regression after a new version publishes and recommend a rollback, and versions publish with a changelog and can be rolled back. A Botlit run record is designed to link to the FluidGrids run the agent triggered, so the reviewer follows the triage answer into the automation that acted on it. FluidGrids shows the failed node with a typed error, its assistant explains why, and it is designed to propose a fix, separating an automation fault from an agent fault. The failing conversation is designed to be saved to Botlit's golden set of cases. An engineer makes the fix in VibeControls as an approved plan, and the new version goes through an evaluation gate designed to run the golden set, optionally in shadow against live traffic, and hold the release with stated reasons until a person accepts, fixes or overrides it.

The outcome it is designed for

Model and instruction changes are tested against known cases before they reach customers, and every incident leaves behind a test that stops it from recurring unnoticed.

The concepts behind it

Botlit

Agent Release Evaluation Gate

Is designed to run saved test cases against a new agent version, compare routing, citations and actions with the live version, and hold the release with reasons.

Works with VibeControls, FluidGrids, BigConsole

Concept pagefor Agent Release Evaluation Gate on the Botlit website (opens in a new tab)

Challenge 5 of 6

Model, compute and tool bills nobody can tie to an outcome

BigConsole(opens BigConsole in a new tab)Botlit(opens Botlit in a new tab)AdapterCloud(opens AdapterCloud in a new tab)FluidGrids(opens FluidGrids in a new tab)VibeControls(opens VibeControls in a new tab)

The problem

Finance asks why AI spend rose again. The answer is spread across three model providers' invoices, a cloud bill that includes a graphics processor cluster for document extraction that sat idle all weekend, and coding assistants' AI accounts paid on engineers' own cards. Each agent's cost per run exists somewhere, but nobody can say what a resolved support case costs with the agent compared with a person, which team's experiment drove the jump, or whether the cheaper model saved more than the escalations it caused.

What it costs

AI budgets are cut or defended on instinct, idle compute burns money unnoticed, and agents that work are hard to justify because their cost per outcome is unknown.

How the products work together

Botlit records what every agent conversation costs and which AI model answered. AdapterCloud shows what each server costs, including graphics processors used for AI work. FluidGrids gathers these figures into BigConsole, which is designed to show the cost of each resolved support case by agent, model and team, and to flag overspending. VibeControls shows which AI accounts engineers' coding assistants run on.

How it works — technical detail

Technical detail

Botlit's execution ledger records tokens, cost and model for every agent, app and workflow run. AdapterCloud attributes cloud and cluster cost records to the resources that incur them, including graphics processor nodes; budgets and anomaly detection are on its roadmap. FluidGrids workflows can pull those records, together with resolved-case counts from the help desk, into BigConsole datasinks, where a unit-economics console is designed to show cost per resolution and per automated action by agent, model and team, track token budgets, alert on overspend and on compute that keeps billing while no runs use it, and cite the record behind each change in an Explain panel. A Botlit agent can then answer a finance question from that console. VibeControls shows which provider keys each coding agent machine uses, and its AI orchestration tasks record tokens and latency, so coding-agent charges on provider invoices can be traced to machines.

The outcome it is designed for

Finance, engineering and operations read one cost per outcome for each agent, idle compute and runaway spend are visible early, and model choices are made on cost and quality together.

The concepts behind it

Challenge 6 of 6

Teams asked to supervise agents nobody trained them for

BuildMyIQ(opens BuildMyIQ in a new tab)TechnoSpam(opens TechnoSpam in a new tab)FluidGrids(opens FluidGrids in a new tab)Botlit(opens Botlit in a new tab)

The problem

An approval step only works if enough people can approve. Today three people are trusted to sign off on agent actions, and one is on leave next week. Support leads who now supervise agents were never shown how to judge an agent's answer, spot a weak citation or reject an action well. Only two engineers know how to write evaluation cases, and everyone else learns by copying their work. Training exists as slide decks, and nobody can say who is ready for which responsibility.

What it costs

Approvals bottleneck on a few people, weak reviews let bad actions through, and adoption stalls because the team cannot supervise more agents than it already has.

How the products work together

BuildMyIQ is designed to train the people who supervise agents on the company's own rules, test them with practice reviews, and certify them. Its list of certified reviewers tells FluidGrids who may approve which actions, and warns when too few people qualify. TechnoSpam gives engineers hands-on lessons and team challenges for building agents, meant to count toward BuildMyIQ's reviewer training.

How it works — technical detail

Technical detail

BuildMyIQ is designed to run role-based paths for agent supervisors, approvers and evaluation reviewers: short lessons on the company's own policies, graded practice reviews built from past agent decisions, including cases Botlit's evaluation gate caught, and a verifiable certificate with an expiry date. Its reviewer roster is designed to show each approval pool against its minimum and hand the current list of certified approvers to FluidGrids, so each approval queue routes decisions only to certified people and warns when a pool falls short. Engineers practice in TechnoSpam through runnable lessons, sandbox-graded coding problems and team hackathons such as an agents and automation sprint; completed TechnoSpam work is designed to count toward BuildMyIQ's evaluation-reviewer path. Leaders get one picture of who is ready to supervise, approve or build.

The outcome it is designed for

The approver bench grows with the automation, every reviewer is trained on the company's own policies, and engineers learn to build and test agents by doing it.

The concepts behind it

How it fits together

How it fits together: from an agent's answer to a trained reviewer

An agent's work touches many teams: the owners of its knowledge, the approvers of its actions, the engineers who change it, finance, and the people who train its supervisors. Each step below is one product doing one job and handing off to the next.

  1. To Botlit: Agreed figures, and who may see them

  2. To FluidGrids: The requested action, with the conversation and sources

  3. To BigConsole: What ran, who approved it and how long it waited

  4. To BigConsole: Computing costs for each server and cluster

  5. To VibeControls: A cost or quality problem to fix

  6. To Botlit: A new agent version, ready for testing

  7. To BuildMyIQ: Real agent mistakes, to practice reviewing

Step 1 of 8: Define the facts agents use

How each hand-off works — technical detail

Technical detail

  1. 1. SemanticFed — Define the facts agents use: Metrics such as seats in use and receipts processed are defined once, and row, column and entity policies are designed to apply to agents as they do to people.Hands to Botlit: Governed figures are designed to reach agents through SemanticFed's MCP server (in progress), with each agent session recorded.
  2. 2. Botlit — Answer and propose: Agents are designed to answer from owned, reviewed knowledge bases with citations and to decide when an action, such as a goodwill credit, is needed.Hands to FluidGrids: The agent triggers a workflow with the conversation and cited passages attached.
  3. 3. FluidGrids — Hold risky actions for a person: An approval queue is designed to apply thresholds per action type, pause risky runs for a certified approver, and resume or stop each run on the decision.Hands to BigConsole: Run outcomes, approvals and waiting times flow through datasink nodes into BigConsole.
  4. 4. AdapterCloud — Attribute compute: Cloud and cluster spend is attributed to the resources that incur it, including graphics processor nodes used for AI work.Hands to BigConsole: Cost records are designed to reach BigConsole through a FluidGrids workflow and datasink.
  5. 5. BigConsole — Read cost and quality per outcome: A unit-economics console is designed to show cost per resolved case by agent, model and team, alert on overspend and cite the record behind each change.Hands to VibeControls: A cost or quality finding is designed to become a change an engineer plans.
  6. 6. VibeControls — Change agents under approved plans: Engineers direct coding agents to change agent instructions, tools or automations; each numbered plan is annotated and approved before any command runs.Hands to Botlit: The new agent version is designed to go to Botlit's evaluation gate before it publishes.
  7. 7. Botlit — Test before release: The evaluation gate is designed to run saved test cases against the new version, compare it with the live one and hold it with stated reasons.Hands to BuildMyIQ: Reviewed agent mistakes are designed to become practice cases in reviewer training.
  8. 8. BuildMyIQ — Certify the reviewers: Role-based paths are designed to train supervisors and approvers on company policy with graded practice reviews, and to certify them with an expiry date.Hands to FluidGrids: The certified reviewer roster is designed to set each approval pool.

Products in this solution

What each product brings

  • Botlit

    Agents grounded in company knowledge

    Botlit is where the company's AI agents are built and run. It keeps each agent's documents, versions and tools in one place, records what every conversation cost, and can hand actions to FluidGrids.

    Technical detail

    Technical detail

    Builds agents with versioned publish and rollback, knowledge bases with index status, tool servers with risk levels and an execution ledger of tokens, cost and model per run; its agents can trigger FluidGrids workflows and query BigConsole dashboards.

    Visit BotlitAll concepts

  • FluidGrids

    Automation with human approval

    FluidGrids runs the company's automations step by step, is designed to pause risky ones for a person to approve from a laptop or phone, explains failures, and sends results to BigConsole for reporting.

    Technical detail

    Technical detail

    Runs versioned workflows with per-node logs, pause, resume and retry, AI failure diagnosis, and datasink nodes that feed BigConsole; an approval queue is designed to pause risky runs for a named approver on desktop or phone.

    Visit FluidGridsAll concepts

  • VibeControls

    Guardrails for coding agents

    VibeControls is where engineers work with AI coding assistants. Every plan is approved by a person before it runs, AI keys stay on each machine under the company's control, and VibeControls does not resell AI usage.

    Technical detail

    Technical detail

    An internal developer platform where coding agents propose numbered plans a person approves, provider keys stay on each agent machine, autonomy is set per agent, and AI runs on your own keys with no resold inference.

    Visit VibeControlsAll concepts

  • BigConsole

    Cost and quality per outcome

    BigConsole is designed to put AI spending next to results on one live board, such as the cost of each resolved support case, and to explain what changed and why.

    Technical detail

    Technical detail

    Consoles built on FluidGrids datasinks, designed with threshold alerts and a cited Explain panel, so agent cost, escalations and compute spend are read against resolved cases.

    Visit BigConsoleAll concepts

  • SemanticFed

    Trusted figures and data rules for agents

    SemanticFed defines key business figures once and is designed to apply the same data rules to AI agents as to staff, keeping a record of what each agent asked for.

    Technical detail

    Technical detail

    A governed semantic layer where metrics are defined once and row, column and entity policies are designed to apply to agents as to people, with each agent session recorded, through its MCP server (in progress).

    Visit SemanticFedAll concepts

  • AdapterCloud

    Compute spend by resource

    AdapterCloud shows every server and cloud account the company runs and what each one costs, including graphics processors running AI work.

    Technical detail

    Technical detail

    Discovers cloud and cluster resources, including graphics processor nodes, and attributes cost records to the resources that incur them; budgets and anomaly detection are on its roadmap.

    Visit AdapterCloudAll concepts

  • BuildMyIQ

    Training and certifying agent supervisors

    BuildMyIQ trains staff who supervise AI agents on the company's own rules, tests them with practice reviews, and certifies them, with lapsing certificates designed to be flagged in time.

    Technical detail

    Technical detail

    Corporate learning with learning paths, graded assessments, skills and verifiable certificates, designed to certify who may supervise or approve agent actions and to flag certificates before they lapse.

    Visit BuildMyIQAll concepts

  • TechnoSpam

    Hands-on agent skills for engineers

    TechnoSpam teaches engineers by doing: hands-on lessons, practice problems checked instantly, and team challenges such as building and testing an AI agent.

    Technical detail

    Technical detail

    Learn-by-doing courses with runnable lessons, coding practice graded in a sandbox and team hackathons, so engineers build and test agents by doing the work.

    Visit TechnoSpamAll concepts

One platform underneath: Burdenoff Workspaces

All eight products run on Burdenoff Workspaces: one sign-in and identity, role-based access, one audit trail of who or what did each thing, and one bill across the products you use.

Concept gallery

Every concept in this solution

18 concepts from 8 products. Each one links to its own page on the product's website, and every view has a link you can share.

Pitch kit

The AI & Automation solution in one minute

Burdenoff brings together what an AI-native team needs to run agents in production: Botlit for agents designed to answer from owned, reviewed knowledge; FluidGrids for automations designed to pause for a certified person when the stakes rise; SemanticFed and VibeControls to keep data access and coding agents inside agreed limits; BigConsole and AdapterCloud to read AI spend per resolved case; and BuildMyIQ and TechnoSpam to grow the people who supervise and build agents.

  • Agents are designed to answer from documents with an owner and a review date, and cite what they used.
  • Risky actions are designed to wait for a certified approver with the evidence on screen; routine ones run straight through.
  • Every agent change is designed to be tested against saved cases before it reaches customers.
  • AI spend is designed to be read as cost per resolved case by agent, model and team.
  • Supervisors and approvers are designed to be trained and certified in BuildMyIQ before they sign off.
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Questions

Frequently asked

Do we have to build our agents in Botlit to use the other products?

No. Each product can be used on its own. FluidGrids can run automations started by other systems, SemanticFed is designed to serve agents built elsewhere, and VibeControls works with many coding assistants. Botlit adds ready-made links: its agents can start FluidGrids automations and read BigConsole boards.

Technical detail

Technical detail

No. Botlit is designed to be the home for agents grounded in company knowledge, but the other products stand on their own: FluidGrids workflows can be started by any system through a webhook or API trigger, SemanticFed's MCP server (in progress) is designed for any MCP-compatible agent, and VibeControls runs many coding agents on your own keys. Botlit adds the documented hand-offs: its agents can trigger FluidGrids workflows and query BigConsole dashboards.

Which AI models and providers does this work with?

You bring your own AI providers and keys. Botlit is designed to work with the major commercial providers and self-hosted models, and VibeControls runs many coding assistants on your keys and does not resell AI usage, so the costs stay visible.

Technical detail

Technical detail

Botlit registers model providers and models per workspace with your own credentials, covering the major commercial providers as well as custom and self-hosted endpoints; today its execution path works with OpenAI-compatible endpoints, and native adapters for other providers are on its roadmap. VibeControls runs many coding agents side by side on your own keys and does not resell model usage, so what each call costs stays visible.

Can an agent take actions such as refunds without a person?

Only if you set it up that way. Actions are designed to pass through FluidGrids, where you decide which ones run automatically and which wait for one or two named people to approve.

Technical detail

Technical detail

Only if you configure it to. In this design, a Botlit agent hands an action to a FluidGrids workflow, and the approval queue decides by threshold what runs straight through and what pauses for one or two named approvers. Botlit also records a risk level for every tool an agent can call; runtime enforcement of its tool and budget policies is on the roadmap, so the thresholds in the workflow are the control point.

How do we review what happened when an agent gets something wrong?

Botlit keeps a record of every agent conversation, including which AI model answered and what it cost, and FluidGrids keeps a step-by-step record of every automation. Each mistake is designed to become a test question that is checked before the next change goes live.

Technical detail

Technical detail

Botlit records every agent, app and workflow run with its status, input and output, tokens, cost, tool calls and the model used, and its operations findings are designed to flag regressions after a version change. FluidGrids keeps a node-by-node run history with typed errors and an assistant that explains failures. The evaluation gate is designed to turn each incident into a saved test case that runs before the next release.

Can agents see data that our staff cannot?

No, that is the design. SemanticFed is designed to hold agents to the same data rules as staff, and all the products share one sign-in, one set of permissions and one record of who did what.

Technical detail

Technical detail

They are designed not to. SemanticFed is designed to apply the same row, column and entity policies to an agent's queries as to a person's and to record each agent session once its MCP server ships; every product runs on Burdenoff Workspaces, with one sign-in, role-based access and one audit trail.

Are these products available today?

Partly. Some products can be used today and others are on a waitlist. This page shows how they are designed to work together; each product's website says what is available now.

Technical detail

Technical detail

The products are at different stages, from generally available to waitlist. This page describes a solution concept: the hand-offs between products are designed to work together, and only some are documented integrations today. Each product's website says what has shipped.

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Where these products also work together

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This is a solution concept: it shows how Burdenoff products are designed to work together in this industry. The images are illustrations of the concepts, not screenshots of the actual products, and every name and figure in them is sample data.