AI Agents and Agentic Workflows
Most tools move data. Agents make decisions. We build AI agents that read your context, call the right tools, handle the exceptions, and finish the job without a human in the loop. Tell us what you want it to do.
These three things are not the same. Most people use them interchangeably. That is why most AI projects disappoint.
Automation moves data from A to B when a trigger fires. It doesn't read context, it doesn't decide anything, and it can't handle exceptions. If the input changes shape, it breaks.
A chatbot responds to input. It might be backed by an LLM, but it doesn't take action. It talks. When the conversation ends, nothing has changed in your systems.
An agent reads a situation, decides what to do, calls tools and APIs, handles the exceptions, and completes a task end-to-end. The output isn't a response. It's a result.
We build the third one.
What We Build
These are the most common starting points. Every one is customized to your stack, your data, and your definition of done.
Qualifies inbound leads and books meetings without a human touching the inbox.
Monitors your inbound channel, scores each lead against your ICP, writes a personalized response, and books the meeting directly. Leads that don't qualify get routed to a nurture sequence automatically. Your reps see only conversations worth having.
Handles tier-1 support at scale without adding headcount.
Reads inbound tickets, classifies intent, resolves common issues using your documentation, and escalates only what genuinely needs a human. The escalation includes full context so agents aren't starting from scratch. Handle time drops. CSAT stays.
Extracts, validates, and routes information from unstructured documents.
Reads contracts, invoices, proposals, and intake forms. Pulls out the fields that matter, validates them against your rules, and pushes clean structured data into the right system. No manual data entry. No copy-paste errors.
Answers questions from your internal knowledge base accurately, with citations.
Embeds your documentation and retrieves the right content at query time. Answers questions with source references so staff stop chasing the same information twice. Stays current as your docs update. Deployed into Slack, a web interface, or wherever your team works.
Gathers, synthesizes, and delivers intelligence on companies, people, or markets.
Given a company name or a research brief, the agent pulls public data, synthesizes a structured summary, and delivers it where you work. Sales teams use it for account research before calls. Leadership teams use it for competitive analysis and market scans.
How We Build
Every agent we ship is built on five layers. Most vendors skip two of them and wonder why their agents hallucinate or break in production.
Orchestration
Controls agent behavior, tool selection, and decision trees. This is where task planning, sub-agent coordination, and retry logic live. Most systems underinvest here.
Tool Layer
The APIs, databases, and external services the agent can call and write to. Scope is intentional -- agents get access to what they need and nothing they don't.
Memory
Short-term context (the current task thread) and long-term knowledge retrieval (your embedded documentation and history). Without this layer, agents forget everything between steps.
Model
The foundation LLM with task-specific prompting, structured output formatting, and guardrails. Model choice is decided per use case, not on instinct.
Data
Your structured records and unstructured content, cleaned, normalized, and embedded. Garbage in is still garbage out -- this layer matters more than most people expect.
How We Build One
Most agents we ship are live within six to ten weeks. Shorter for retrieval-style agents on clean data. Longer when the integrations are messy or the policies are complex. Whatever the timeline, the phases are the same.
Discovery
Days 1-3
Define the exact job. The success criteria. What the agent is allowed to do without asking. Where humans must stay in the loop. Most failed agent projects skipped this step or rushed it.
Architecture
Days 4-7
Pick the model. Define the tools the agent will use. Decide what goes in the context window versus what gets retrieved at runtime. Decide the escalation paths. Output is a written spec you sign off on.
Prototype
Weeks 2-3
Build a v1 that handles the happy path. Wire up the real tools. Test against a handful of real inputs from your business. The prototype works on the average case. It will fail on the edges. That is expected.
Hardening
Weeks 4-6
The unglamorous part of the work. We run the agent against difficult inputs, ambiguous inputs, malicious inputs. Add guardrails. Tune prompts. Build the escalation flows. This is what separates a demo from a production system.
Launch and observe
Week 6 onward
Deploy with monitoring on day one. The first two weeks are dense. We watch what the agent decides, where it hesitates, where it gets confused. Then we tune. Most agents need one to three rounds of post-launch tuning before they stabilize.
Discovery
Days 1-3
Define the exact job. The success criteria. What the agent is allowed to do without asking. Where humans must stay in the loop. Most failed agent projects skipped this step or rushed it.
Architecture
Days 4-7
Pick the model. Define the tools the agent will use. Decide what goes in the context window versus what gets retrieved at runtime. Decide the escalation paths. Output is a written spec you sign off on.
Prototype
Weeks 2-3
Build a v1 that handles the happy path. Wire up the real tools. Test against a handful of real inputs from your business. The prototype works on the average case. It will fail on the edges. That is expected.
Hardening
Weeks 4-6
The unglamorous part of the work. We run the agent against difficult inputs, ambiguous inputs, malicious inputs. Add guardrails. Tune prompts. Build the escalation flows. This is what separates a demo from a production system.
Launch and observe
Week 6 onward
Deploy with monitoring on day one. The first two weeks are dense. We watch what the agent decides, where it hesitates, where it gets confused. Then we tune. Most agents need one to three rounds of post-launch tuning before they stabilize.
Straight Talk
Security and Data
AI work raises real concerns about data, compliance, and what happens when the model provider changes the rules. We answer these on every call. Here are the answers, written down.
It depends on the model. For most engagements we use providers with zero data retention agreements in place (Claude via Anthropic, GPT via OpenAI with the DPA, both configurable for no training on your data). For sensitive workloads we deploy open-source models on infrastructure you own or rent (AWS, Azure, GCP, or your own VPC). The choice is yours and we will explain the trade-offs honestly.
Yes. We deploy agents on your own cloud infrastructure when the use case requires it. Open-source models (Llama, Mistral, Qwen and similar) running on your VPC, behind your firewall, with no external API calls. This costs more to run and slows down iteration, but it removes the data-leaves-your-network concern entirely.
Architecture decisions get made with compliance in mind from day one, not bolted on at the end. We work within your existing compliance boundary, which usually means selecting model providers with the right certifications, designing data flows that respect retention rules, and producing the audit trail your compliance team needs. We are not a compliance auditor, but we are familiar with what the auditors look for.
Two protections. First, every agent is built to be model-agnostic where possible. Switching from GPT to Claude or from Claude to an open-source model is usually a config change, not a rebuild. Second, we monitor cost per agent action so you see drift early. Provider terms do change. We have moved clients off providers before. We will do it again if we have to.
We use a principle of minimum access. Engineers only see the data they need to build their part. Sensitive data stays in your environment whenever possible. We are happy to sign your security questionnaire, your DPA, and any agreement your legal team needs. Most engagements include a short security review at kickoff.
Every agent action is logged: input, decision, tool calls, output, latency, cost. The logs live in your infrastructure or in a logging stack we deploy for you. Access is gated. You can review what the agent did, why, and when, and you can revoke access at any time. There is no Arius backdoor.
Common Questions
Grouped by where they usually come up in the conversation. Each answer is written the way we actually answer it on a call.
Six to ten weeks is typical for an agent with real complexity. Retrieval-style agents on clean data can be three to five weeks. Anything involving sensitive data, complex integrations, or multi-step decision making lands at the longer end. We give a real estimate after the discovery phase, not before.
Yes, and we usually recommend it. Companies who try to build five agents at once almost always end up with five mediocre ones. Pick the one with the highest leverage, ship it, learn from it, then build the next one with everything you learned from the first.
Yes. The output of the discovery phase is a written spec that defines the agent's job, its tools, its decision boundaries, and its escalation rules. You sign off on it before any code gets written. This is the document that protects both sides from scope drift later.
Agents need three things on an ongoing basis: monitoring (logs, costs, accuracy), tuning (prompts and routing rules change as the agent meets new edge cases), and updates (when models change or new tools get added). Most clients keep us on a small retainer for this. Some bring it in house after the first six months. Both work.
Claude (Anthropic), GPT (OpenAI), and Gemini (Google) for managed work. Llama, Mistral, and Qwen for self-hosted work. The choice depends on the job. Claude tends to win on reasoning and instruction following. GPT tends to win on tool use ergonomics. Open-source wins on cost and control. We pick per use case, and sometimes we use two models in one agent if the cost-quality trade-off justifies it.
Rarely. For most business agents, smarter prompting and good retrieval beats fine-tuning, costs less, and is easier to maintain. Fine-tuning makes sense for narrow tasks with very high volume and stable patterns. We will tell you when that fits and when it does not.
We use LangChain and LlamaIndex where they help. We write custom code where they get in the way. The framework is not the product. The agent is. We pick whatever lets us ship a stable agent fastest, and we are not religious about it.
Yes. Most agents we build integrate with CRMs (HubSpot, Salesforce, Pipedrive), help desks (Intercom, Zendesk), email and calendar, knowledge bases (Notion, Confluence, Google Drive), and custom APIs. If you have an API, we can use it. If you do not, we can build one as part of the engagement.
Typical agent builds land somewhere between a focused four-week project and a multi-month engineering engagement. Simple retrieval agents are at the lower end. Complex agents with multiple integrations, custom guardrails, and high stakes are at the higher end. We share specific numbers after the discovery call once we have scoped what you actually need.
Two cost lines. Model usage (per token or per call to the model provider) and infrastructure (hosting, monitoring, data storage). For a typical agent doing a few thousand actions per month on a managed model, monthly run cost is usually in the low hundreds to low thousands of dollars. We model this for you as part of the architecture phase so there are no surprises.
Fixed-fee for the build. Optional retainer for ongoing maintenance, tuning, and new agent work. No revenue share, no per-seat fees, no surprise charges. The economics of AI work are not stable enough to commit to anything more complex than that yet.
You do. Code, prompts, configurations, and any custom integrations are yours. We do not retain rights to them. If you want to take the project in house after launch, you can. We will hand over everything documented and walk your team through it.
Tell Us What You Want It To Do
Bring us a process that takes too long, a task your team hates doing, or a workflow that breaks every time someone goes on holiday. We'll scope it, architect it, and build it to production standard.
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