What agents can automate

Research & synthesis

Browse the web, read PDFs, aggregate sources, produce structured summaries - in minutes, not hours.

Document processing

Ingest contracts, invoices, reports. Extract fields, validate data, route for approval or rejection.

Code generation

Scaffold boilerplate, write tests, refactor to spec. Commits PRs with explanations for review.

Data extraction

Scrape, parse, normalize, and load structured data from websites, APIs, and unstructured files.

Customer comms

Draft replies, handle tier-1 support, send follow-ups, and escalate with full context attached.

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Case Study - Customer Operations

Agent that handles 82% of tier-1 customer support without human escalation.

Sequential pipeline: intent classification → KB lookup → response drafting → send or escalate. Integrates with Zendesk, Shopify, and a custom returns API. Runs 24/7 with a 1.8s average response time.

82%

Auto-resolved

1.8s

Avg response time

94%

CSAT score

60%

Support efficiency reduction

By the numbers

10×

Throughput with parallel agent patterns vs. single-threaded workflows

< 2s

Median tool call latency including API round-trip and parsing overhead

94%

Task completion rate without human escalation across deployed agents

60%

Reduction in operational headcount resource within 6 months of deployment

A different approach

How we build differently

The usual way

Scripts that break when page structure changes

Brittle RPA bots with no understanding of context

Hardcoded workflows that can't adapt to edge cases

Hours of manual QA to catch what automation missed

No visibility into why a task succeeded or failed

With StartxLabs

✓

LLM-powered agents that understand intent, not just structure

✓

Tool-use patterns that adapt to changing APIs and data shapes

✓

Orchestration logic that handles exceptions without crashing

✓

Confidence thresholds that route edge cases to humans gracefully

✓

Full observability: every decision logged with inputs and outputs

Engagement model

From kickoff to production agent

Discovery

Map the target workflow, data sources, and tool integrations. Define success metrics.

Architecture

Choose agent pattern (single / sequential / parallel) and design tool schemas.

Build

Implement agent loops, tool definitions, human-in-the-loop gates, and memory.

Eval & harden

Run evals against real scenarios. Tune thresholds, prompts, and fallback paths.

Production

Deploy with observability, alerting, and a handoff runbook for your team.

Common questions

FAQ

What's the difference between an AI agent and an AI chatbot?

A chatbot responds to messages. An agent takes actions - it can call APIs, run code, read and write files, send emails, and coordinate with other agents. The key difference is tool use and autonomous execution across multi-step tasks.

How do you handle tasks that fail mid-way?

Every agent we build includes error handling, retry logic, and configurable fallback paths. For irreversible actions, we use transactional patterns. Failed or ambiguous runs are escalated to a human queue with full context attached.

Can agents work with our internal systems that don't have public APIs?

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

How long does it take to build a production agent?

A focused, well-scoped agent takes 4–6 weeks from discovery to production deployment. Complex multi-agent systems with many tool integrations typically run 8–12 weeks. We always ship a working prototype in week 2.

What frameworks do you use?

We're framework-pragmatic. We've shipped agents using LangGraph, CrewAI, OpenAI Assistants, and custom orchestration loops. The right choice depends on your latency requirements, debugging needs, and the complexity of your agentic graph.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

What agents can automate

Research & synthesis

Browse the web, read PDFs, aggregate sources, produce structured summaries - in minutes, not hours.

Document processing

Ingest contracts, invoices, reports. Extract fields, validate data, route for approval or rejection.

Code generation

Scaffold boilerplate, write tests, refactor to spec. Commits PRs with explanations for review.

Data extraction

Scrape, parse, normalize, and load structured data from websites, APIs, and unstructured files.

Customer comms

Draft replies, handle tier-1 support, send follow-ups, and escalate with full context attached.

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Case Study - Customer Operations

Agent that handles 82% of tier-1 customer support without human escalation.

82%

Auto-resolved

1.8s

Avg response time

94%

CSAT score

60%

Support efficiency reduction

By the numbers

10×

Throughput with parallel agent patterns vs. single-threaded workflows

< 2s

Median tool call latency including API round-trip and parsing overhead

94%

Task completion rate without human escalation across deployed agents

60%

Reduction in operational headcount resource within 6 months of deployment

A different approach

How we build differently

The usual way

Scripts that break when page structure changes

Brittle RPA bots with no understanding of context

Hardcoded workflows that can't adapt to edge cases

Hours of manual QA to catch what automation missed

No visibility into why a task succeeded or failed

With StartxLabs

✓

LLM-powered agents that understand intent, not just structure

✓

Tool-use patterns that adapt to changing APIs and data shapes

✓

Orchestration logic that handles exceptions without crashing

✓

Confidence thresholds that route edge cases to humans gracefully

✓

Full observability: every decision logged with inputs and outputs

Engagement model

From kickoff to production agent

Discovery

Map the target workflow, data sources, and tool integrations. Define success metrics.

Architecture

Choose agent pattern (single / sequential / parallel) and design tool schemas.

Build

Implement agent loops, tool definitions, human-in-the-loop gates, and memory.

Eval & harden

Run evals against real scenarios. Tune thresholds, prompts, and fallback paths.

Production

Deploy with observability, alerting, and a handoff runbook for your team.

Common questions

FAQ

What's the difference between an AI agent and an AI chatbot?

How do you handle tasks that fail mid-way?

Can agents work with our internal systems that don't have public APIs?

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

How long does it take to build a production agent?

What frameworks do you use?

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

AI Agent Development

Automate the workflows
your team still does manually.

We build autonomous AI agents that browse, write, execute code, and coordinate with your systems. Real automation - not chatbots wearing a hard hat.

Build an agent See examples

What agents can automate

Research & synthesis

Browse the web, read PDFs, aggregate sources, produce structured summaries - in minutes, not hours.

Document processing

Ingest contracts, invoices, reports. Extract fields, validate data, route for approval or rejection.

Code generation

Scaffold boilerplate, write tests, refactor to spec. Commits PRs with explanations for review.

Data extraction

Scrape, parse, normalize, and load structured data from websites, APIs, and unstructured files.

Customer comms

Draft replies, handle tier-1 support, send follow-ups, and escalate with full context attached.

Agent architecture patterns

The right architecture depends on task complexity, speed requirements, and how many steps are involved.

Simple tasks

Single agent

One LLM with a defined tool set handles a single, well-scoped task from start to finish.

User request

→ LLM reasons

→ Tool calls

→ Result

e.g. Document Q&A, form filling, data lookups

Multi-step workflows

Sequential pipeline

Agents are chained - each agent's output is the next agent's input. Deterministic ordering, easy to debug.

Input

→ Agent A (extract)

→ Agent B (classify)

→ Agent C (output)

e.g. Contract review → risk scoring → summary generation

Scale & speed

Parallel agents

Multiple agents run concurrently, coordinated by an orchestrator that merges results. 10× throughput.

Orchestrator

→ Agent 1 + Agent 2 + Agent 3 (parallel)

→ Merge

→ Output

e.g. Multi-source research, batch document processing

Tools & integrations

Agents are only as powerful as the tools they can use. Here's what we connect them to.

Web browsingCode executionREST APIsWebhooksSQL databasesVector storesEmailCalendarSlack / TeamsCRM (Salesforce, HubSpot)File systemsGitHubPDF / DOCX parsingSpreadsheetsZapier / Make

Need a custom tool? We write tool definitions and connection adapters for any API or internal system. If it has an endpoint, the agent can call it.

Human-in-the-loop controls

Autonomy doesn't mean unaccountable. Every agent we build has explicit human oversight mechanisms.

Approval gates

High-stakes actions (sending emails, making payments, modifying records) pause and require explicit human sign-off before proceeding.

Confidence thresholds

Agents declare confidence scores. Below threshold, the task is routed to a human queue instead of auto-executing.

Audit logging

Every tool call, decision branch, and output is logged with timestamps and inputs. Replay any agent run for compliance review.

Rollback mechanisms

Actions are transactional where possible. Failed or flagged runs can be reversed without manual intervention.

Observability

You can't manage what you can't measure. Every agent ships with a full observability stack.

Agent run dashboard - live metrics

Token usage per run

avg 2,840 tokens / task

↓ 12% after prompt optimization

Tool call success rate

98.3% successful

↑ vs 91% at launch

Task completion rate

94.1% fully automated

5.9% escalated to human

Avg latency per step

1.2s per tool call

p95: 3.8s

Resource per run

0.041 avg

Target: <0.05

Escalation reasons

Top: low confidence (62%)

Ambiguous input (21%), API error (17%)

Case Study - Customer Operations

Agent that handles 82% of tier-1 customer support without human escalation.

82%

Auto-resolved

1.8s

Avg response time

94%

CSAT score

60%

Support efficiency reduction

By the numbers

10×

Throughput with parallel agent patterns vs. single-threaded workflows

< 2s

Median tool call latency including API round-trip and parsing overhead

94%

Task completion rate without human escalation across deployed agents

60%

Reduction in operational headcount resource within 6 months of deployment

A different approach

How we build differently

The usual way

Scripts that break when page structure changes

Brittle RPA bots with no understanding of context

Hardcoded workflows that can't adapt to edge cases

Hours of manual QA to catch what automation missed

No visibility into why a task succeeded or failed

With StartxLabs

✓

LLM-powered agents that understand intent, not just structure

✓

Tool-use patterns that adapt to changing APIs and data shapes

✓

Orchestration logic that handles exceptions without crashing

✓

Confidence thresholds that route edge cases to humans gracefully

✓

Full observability: every decision logged with inputs and outputs

Engagement model

From kickoff to production agent

Discovery

Map the target workflow, data sources, and tool integrations. Define success metrics.

Architecture

Choose agent pattern (single / sequential / parallel) and design tool schemas.

Build

Implement agent loops, tool definitions, human-in-the-loop gates, and memory.

Eval & harden

Run evals against real scenarios. Tune thresholds, prompts, and fallback paths.

Production

Deploy with observability, alerting, and a handoff runbook for your team.

Common questions

FAQ

What's the difference between an AI agent and an AI chatbot?

How do you handle tasks that fail mid-way?

Can agents work with our internal systems that don't have public APIs?

Yes. We write custom tool adapters for internal databases, legacy REST services, and internal tooling. If your system has an endpoint, a CLI, or a webhook - we can make it a tool the agent can call.

How long does it take to build a production agent?

What frameworks do you use?

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

Automate the workflowsyour team still does manually.

What agents can automate

Agent architecture patterns

Tools & integrations

Human-in-the-loop controls

Observability

Agent that handles 82% of tier-1 customer support without human escalation.

How we build differently

From kickoff to production agent

FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Automate the workflowsyour team still does manually.

What agents can automate

Agent architecture patterns

Tools & integrations

Human-in-the-loop controls

Observability

Agent that handles 82% of tier-1 customer support without human escalation.

How we build differently

From kickoff to production agent

FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Automate the workflowsyour team still does manually.

What agents can automate

Agent architecture patterns

Tools & integrations

Human-in-the-loop controls

Observability

Agent that handles 82% of tier-1 customer support without human escalation.

How we build differently

From kickoff to production agent

FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Automate the workflows
your team still does manually.

Ready to build your
next digital product?

Tell us about
your project

Automate the workflows
your team still does manually.

Ready to build your
next digital product?

Tell us about
your project

Automate the workflows
your team still does manually.

Ready to build your
next digital product?

Tell us about
your project