Feature · AI Agent Workflow Builder

An AI agent builder that writes to the record, not next to it.

Most AI agent builders are a chat window with a toolbelt and a prayer that the model will stay on the rails. What a sales ops team actually needs is different: a visual canvas where an agent reads the opportunity record, calls the pricing lookup, drafts the follow-up in the rep voice, routes the deal to the right AE if it crosses a threshold, and leaves the audit trail finance will ask about in September. Strkr AI runs inside the flow canvas with typed inputs, typed outputs, approval gates, and the same role-based permissions that gate every other write to the CRM.

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What a working agent builder has to do

The seven jobs most AI agent builders skip.

A real agent builder has to do more than wrap a chat completion with a tool list. The agent has to read a typed record, call a typed tool with validated inputs, hold state across steps, ask for approval when stakes cross a threshold, retry when a downstream API is slow, cite the records that drove a decision, and write back to the CRM with the same permissions as a human user. The ones that skip steps are demos. The ones that cover them end up running.

Typed record reads

Every field, every custom object, cited.

The agent reads the opportunity, the account, the contact, the custom objects, and the activity log as a typed input. Each read is logged and the agent output cites the records that drove the decision. The common failure where an AI agent answers from the title and misses the custom field that matters does not happen.

Typed tool calls

Every tool a schema, every input validated.

Tools are declared with a JSON schema. The agent cannot call a tool with a malformed payload. The call lands on the flow run page with the inputs, the response, and the duration. A pricing lookup tool with a bad SKU returns a validation error, not a 500 from the vendor the agent misread.

State across steps

The agent remembers what it already did.

Multi-step reasoning carries explicit state. The agent that reads the opportunity, calls the usage API, drafts the email, and schedules the task does not re-read the opportunity three times. State lives on the flow run and is inspectable mid-run, so the operator debugging a stalled agent sees the exact value at step 4.

Approval gates

The agent pauses when stakes cross a line.

Approval nodes gate writes above a threshold: outbound email over a dollar value, record changes to a locked field, external calls that incur cost. The approver gets the context, the proposed write, and the receipts in one surface. The agent waits, logs the approval, and executes. A rogue agent cannot write six figures of damage on a Friday.

Retry and backoff

Downstream API hiccups do not blow up the run.

Every outbound tool node runs with configurable retry, exponential backoff, and timeout. A slow HubSpot write retries three times with jitter rather than crashing the agent run. The run page shows the retry count and the final response. A one-second blip does not derail a 20-step agent.

Audit on every write

Finance can trace the agent action in September.

Every tool call, every record read, every record write, every approval, and every model response lands in the audit log keyed on the flow run ID. A finance query like "show every AI agent write to Opportunity in Q2" is a filter on the audit page. The compliance posture is HIPAA with BAA available and SOC 2 Type II in progress.

Permission inheritance

The agent writes with the acting user permissions.

An agent run by a rep sees the records the rep can see. Field-level, row-level, and object-level permissions apply to agent output the same way they apply to a form. The common failure where an AI agent reads data a human would not have access to does not happen because the authorization layer is shared.

The builder itself

A visual canvas with real engineering primitives.

A no-code canvas with the depth to run production agents. Operators drag nodes, wire inputs to outputs, set approvals, test on real data, and promote the flow from draft to live with an audit trail. Developers drop into TypeScript nodes when the use case needs it. Both paths coexist on the same canvas.

Reasoning node

A model call with typed inputs and outputs.

The reasoning node accepts a system prompt, a user prompt, a tool list, and a context payload. Outputs are structured JSON validated against a schema. The node runs the model, parses the output, and routes based on structured decisions. The common failure where free-form model output breaks the next step does not happen.

Tool catalog

Record read, record write, HTTP, email, SMS, flow.

Tools include typed record reads and writes against every Strkr object, HTTP calls, email send through the compliant mail service, Strkr Messaging send, and nested flow invocation. Each tool carries a schema and a cost budget. The catalog is extensible with custom tool nodes authored in TypeScript.

Branch node

If-then-else with inspectable state.

Branch nodes read the structured output of the reasoning node and route to one of several downstream paths. Each branch is inspectable in the run view: the condition, the evaluated value, the branch taken. Debugging a branch that fired wrong is a two-minute investigation, not a two-day one.

Human-in-the-loop

An approval card the human actually acts on.

Human-in-the-loop nodes surface an approval card on the assignee home. The card carries the proposed action, the context, and the receipts. Approve, edit, or reject. The flow resumes with the acting user and timestamp recorded. The common failure where approvals land in an email inbox and get missed does not happen.

Live test on real data

Dry-run against the last 30 days of records.

The dry-run mode runs the flow against the last 30 days of qualifying records without writing. Output is a diff: what the agent would have done. The operator reviews 50 cases, approves, and the flow goes live. The first production run is boring because the dry-run already covered it.

Promotion with audit

Draft to review to live, each step stamped.

A flow moves from draft to review to live with explicit promotions. Each promotion records the acting user, the diff from the prior version, and the timestamp. A rollback is a one-click action that restores the prior version. The common failure where the live flow drifts without anyone knowing which change broke it does not happen.

What ships out of the box

Six prebuilt agent templates teams actually run.

A new team can clone a template and ship a running agent on day one. The template carries the canvas, the tool wiring, the approval gates, and the dry-run config. The team edits the prompt and the field mapping and promotes to live. The seven weeks of agent plumbing most teams would otherwise burn gets compressed to an afternoon.

Deal risk scorer

Nightly scan, risk tier on every open deal.

The scorer reads every open opportunity, calls the usage API, scores the risk from stage velocity, days-since-activity, champion engagement, and non-standard clause flags, and writes the risk tier back on the opportunity record. The manager running the week starts from a sorted risk list, not a vibe. Yellow flags land at day 7, not day 70.

Follow-up drafter

Rep-voice email on every stalled deal.

For every deal stalled past the stage SLA, the drafter reads the activity history, drafts a follow-up citing the last two touchpoints, and surfaces the draft in the rep home. The rep approves or edits in one minute. The common failure where every AI-drafted email reads like a generic marketing paragraph does not happen because the drafter learns the rep sent-mail history.

Lead triage agent

Score, route, enrich, before the SDR sees it.

The triage agent catches inbound leads, enriches with firmographic data, scores against the ICP, routes to the correct SDR, drafts the opener, and creates the task. The SDR picks up a lead that is already triaged with the opener ready. First-touch time drops from hours to under a minute.

Call summary agent

Transcribe, extract, create tasks, update the record.

After a recorded call, the agent transcribes, extracts the key moments, drafts the follow-up with quoted commitments, creates tasks for every explicit next step, and updates the opportunity with new discovery. The post-call paperwork disappears. The rep ships the follow-up in under five minutes.

Renewal risk watcher

Daily scan of accounts within 90 days of renewal.

The watcher scans accounts inside the 90-day renewal window, pulls usage trend, support ticket volume, and executive sponsor engagement, and surfaces at-risk accounts on the CS lead home. The CS lead works a sorted list, not a hunch. Renewal attrition shows a measurable improvement over the first full cycle.

Pricing approval agent

Non-standard clause detection and routing.

The agent reads a proposed deal, detects non-standard discount, non-standard terms, and non-standard clauses against the pricing catalog, and routes approval to the correct approver (manager, finance, legal). The approver gets the proposed deal, the delta from standard, and the receipts in one surface. The approval cycle shortens from days to hours.

Observability and operations

The parts that keep agents working after launch.

A built agent is one-tenth of the project. The nine-tenths is keeping it working against drifting data, slow APIs, prompt regressions, cost spikes, and compliance audits. Strkr ships the observability surface and the operational tooling that most agent builder vendors leave as an exercise for the customer.

Run page

Every step, every input, every output.

Every agent run opens on a page that shows the step graph, the inputs and outputs of each node, the model tokens used, the tool calls made, and the final writes. A debug session that would otherwise take hours of log-grepping is a two-minute scroll. The run page is the primary debugging surface, not a secondary one.

Cost budget

Token and tool spend per run, per flow, per tenant.

Every run carries a cost budget. The budget covers tokens, outbound API costs, and SMS send cost. Admin sets per-flow and per-tenant ceilings. The run stops when the ceiling hits and an alert fires on the admin home. The common failure where an agent runs on a loop and burns $4,000 of tokens overnight does not happen.

Alerting on failure

The on-call sees the alert, not the user.

Agent failures (tool error, timeout, model refusal, approval timeout) fire an alert to the configured channel. The alert carries the run ID, the failing node, and the response payload. The on-call opens the run page from the alert and starts debugging in context. The user who triggered the flow does not see the failure first.

Prompt versioning

The exact prompt that ran on each run, forever.

Every reasoning node run stores the exact prompt, model version, temperature, and tool list used. A regression (today the agent is answering wrong, yesterday it was fine) is a diff on the prompt history. The common failure where a prompt change ships and nobody can roll back cleanly does not happen.

A/B shadow

Run two prompts in parallel, compare outputs.

Shadow mode runs a candidate prompt alongside the live prompt on the same inputs without writing the candidate output. The operator reviews a 100-run sample and promotes the candidate if it beats the live prompt on the scoring rubric. Prompt improvements ship with evidence, not vibes.

Compliance posture

HIPAA BAA available, SOC 2 Type II in progress.

Strkr signs HIPAA Business Associate Agreements for healthcare tenants. SOC 2 Type II audit is in progress with the Type I report available on request. Agent runs that process PHI route through the compliant model path with sealed logs. The audit log carries the full chain of custody for regulated workloads.

An AI agent builder that writes to the record, not next to it.

Strkr AI runs on the flow canvas with typed inputs, typed outputs, approval gates, and permission inheritance. Clone a prebuilt agent template, dry-run against real data, promote to live. $50 per seat flat with no per-prompt credit meter.

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Common questions

What buyers ask about this feature.

How is this different from a general-purpose AI agent builder?

A general-purpose agent builder gives you a model, a tool list, and a canvas. Strkr gives you a model, a tool list, a canvas, a typed CRM data model, approval gates, permission inheritance, cost budgets, prompt versioning, and an audit log that answers compliance questions. The agents you build on Strkr are production-grade from the first draft because the plumbing problems that usually eat seven weeks of agent build-out are already solved. The tradeoff is that Strkr agents work best inside a CRM and revenue workflow; a general-purpose agent builder makes sense for things that are not CRM-shaped.

Can non-technical operators build agents on Strkr?

Yes. The canvas is drag-and-drop with typed inputs and outputs. An ops lead who has shipped a lead-routing flow can ship an AI agent flow. Developers can drop into TypeScript nodes for custom logic when the use case needs it. Both paths coexist, so a team can start with no-code and selectively graduate specific nodes to code as the complexity warrants. The common failure where an AI agent builder is either too shallow for real work or too complex for ops to touch gets avoided.

Does the AI agent respect our permissions and compliance posture?

Yes. Every agent runs with the acting user permissions. Field-level, row-level, and object-level permissions apply to agent reads and writes the same way they apply to a form view. Every tool call, record read, record write, approval, and model response lands in the audit log keyed on the flow run ID. Strkr signs HIPAA Business Associate Agreements for tenants in regulated industries, and SOC 2 Type II audit is in progress with the Type I report available on request.

How does the AI agent handle approvals when stakes are high?

Approval nodes gate writes above a threshold: outbound email over a dollar value, record changes to a locked field, external calls that incur cost. The approver gets the proposed action, the context, and the receipts on a single card on their home. Approve, edit, or reject. The flow resumes with the acting user and timestamp recorded. The default posture is human-in-the-loop; fully autonomous mode is explicit, scoped to specific flow types, and audit-logged separately.

What happens when a tool call fails or a model refuses?

Every tool node runs with configurable retry, exponential backoff, and timeout. A tool failure that persists past the retry budget surfaces on the run page and fires an alert to the configured channel. A model refusal (safety filter, timeout, context overflow) routes to a fallback branch that can either hand the task to a human, route to a different model, or log and halt. The common failure where an agent silently drops a task because a downstream API blipped does not happen.

Does the AI agent builder ship with a credit meter?

Strkr AI ships on every paid tier at $50 per seat with no per-prompt credit meter. The rep running an agent twenty times a day costs the same as the rep running it twice a day. Admin can set cost budgets per flow and per tenant to prevent runaway misconfiguration (an agent in a loop should not burn $4,000 of tokens overnight), but there is no vendor-imposed credit tier that unlocks premium agent behavior behind a paywall. The seat price on the pricing page is the full price for the AI capability.

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