Solution

Agents & Workflow Automation

Agentic workflows that read, write, and act across your existing tools — with human-in-the-loop where it matters.

Tool-using agents inside your systems

We build agents that do work, not just chat. They call your APIs, update your records, trigger your workflows, and hand off to humans when confidence is low. Focus is on reliability: scoped permissions, deterministic steps where possible, and traceable execution for every run.

Outcomes

60-90%

reduction in manual task time

100%

auditability per run

Weeks

from prototype to production agent

How we build it

Our approach.

Decompose the workflow

We map the task as a graph: deterministic steps, decision points, and the places where an LLM genuinely adds value. Most of an agent should be boring code.

Define tools + guardrails

Typed tool schemas, scoped credentials, dry-run modes for destructive actions, and human approval gates wherever risk is high.

Evaluate end-to-end

Gold tasks run the full agent. We score task success, step efficiency, and cost per successful run — not just individual turns.

Deploy on durable infra

LangGraph, Temporal, Step Functions, or Durable Functions. Runs survive restarts, retries are idempotent, and every action is traced.

Capabilities

What you get.

Tool definitions and schemas for your APIs

Planner / executor architectures with retries

Human-in-the-loop approval gates

Scoped credentials and audit trails

Long-running workflows with state checkpointing

Integration with MCP servers for standardized tool access

What it looks like

Production-shaped, from day one.

agent.ts

// Tool-using agent with HITL approval on writes
const agent = createAgent({
  model: "claude-opus-4-6",
  tools: [crm.search, crm.update, slack.notify],
  guardrails: {
    approvalRequired: ["crm.update"],
    dryRun: false,
    maxSteps: 12,
  },
  onStep: (step) => trace.log(step),
})

const result = await agent.run(task, { user, runId })
// -> { status, steps, cost_usd, approvals[] }

Architecture

A proven shape for this solution.

We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.

Orchestration (LangGraph, Temporal, Step Functions, Durable Functions)

Tool layer (internal APIs + MCP servers)

State store for in-flight runs

Evaluation harness for end-to-end tasks

Observability per run (traces, tokens, latency, cost)

Use cases

Where this shows up.

Back-office automation: ticketing, CRM updates, data entry
Dev workflows: PR review, test generation, runbook execution
Data operations: enrichment, validation, reconciliation
Coach workflow: program updates from session notes

Stack

What we use.

We’re not religious about tools. We pick what fits your constraints and team.

LangGraph

Temporal

AWS Step Functions

Azure Durable Functions

Anthropic Tool Use

OpenAI Functions

MCP

In production

Shipped examples.

Coaching & Wellness

Coach session intelligence & program updates

Turned coaching session notes and history into structured program updates, progress summaries, and next-action recommendations.

OpenAIAnthropicLangGraphPostgrespgvector

Common questions

What teams usually ask.

How do you make agents reliable?

Keep the LLM scope narrow. Most steps are deterministic code. We add retries, idempotent tool calls, checkpointed state, and approval gates on anything destructive.

What happens when an agent gets it wrong?

Every run is traced end-to-end. You can replay, diff against a prior run, and add the failure to the eval set so it can't regress.

Can agents use our existing APIs?

Yes — through direct tool definitions or, increasingly, through MCP servers that expose your systems to any MCP-aware agent.

Keep exploring

Ready to accelerate your tech growth?

Schedule your free consultation today and let's discuss how we can help your business scale efficiently.