Build Agents Your Users Can Trust

We take you from idea all the way to a governed agent running in production, in four stages. The Find phase takes three to five days, where we rank your use cases by ROI. We then move to Build, which takes two to four weeks, and deliver an agent with the tools, context, memory, orchestration to operationalize a workflow. From there, Validate and Deploy, which is two to three weeks, where we regression-test the agent for accuracy and deploy it with integrations and governance. We finally move to Improve, which is the lifecycle maintenance, and we deliver a weekly drift and cost report.

There are six types: chat agents, copilots, workflow agents, autonomous task agents, long-running agents, and ambient, or event-driven, agents. Each one takes a different engineering approach, and we’ve shipped production systems in all six. One example is a natural-language data chat agent with 95%+ query accuracy. Another is a setup with 10 agents managed from a single control point. We match the agent type to your problem instead of forcing one kind of agent to do everything.

Start with four questions. What triggers the agent? How long does it run? How much control does a human keep? And how does the work flow? Chat agents are great for asking questions over your data. Copilots fit work a person needs to approve. Workflow agents are great for set processes, with human checkpoints. Autonomous task agents take a goal and plan their own steps. Long-running agents work for hours or even days. And ambient agents kick off on an event and keep a full record. Our Find stage maps your use cases to the right type.

Usually five to eight weeks from kickoff to going live. It runs in four stages: Find, three to five days, to rank use cases by ROI. Build, two to four weeks, to get a working agent on a real workflow. Validate and Deploy, two to three weeks, for testing and rollout. Then Improve, which is ongoing. A simple single-tool chat agent lands at the shorter end. If the agent acts on transactional systems, it takes longer.

It depends on a few things: how many tools and systems the agent connects to, how much data access it needs, and how much validation your use case requires. Our Find stage takes three to five days and gives you a ranked list of use cases with ROI up front. A first working agent typically takes two to four weeks to build.

An AI agent that passes all internal tests can still hallucinate, fail during critical handoffs, or silently drive-up compute costs once customers start using it. So we treat deployment as a lifecycle. We regression-test the accuracy of the agent prior to launch. After that, the Improve stage keeps watching behavior, cost, and latency, and feeds what it learns back into the agent. On one enterprise configuration platform, we added a validation layer to check all changes before they are applied, which saw zero recorded hallucinations.

There are really four things to watch for. First, prompt injection, where someone hides instructions in an email or document the agent reads, and the agent follows them. Second, giving the agent more tool access than it needs. Third, sensitive data leaking out. And fourth, the agent taking a big action with nobody checking it. So we design around all four. We limit all agents’ access to only those tools that are necessary for their function and make sure that no agent has more power than necessary. We automatically catch and redact personal data. Before any action runs, we check that it’s the right kind of action, within a safe range, and actually what the user intended. And for anything high-impact, a person has to approve it first.

Prompt injection is when an agent reads something like an email, a web page, or a document that has hidden instructions in it, and then follows them. We defend in layers. Untrusted content is segmented from the system instructions. Inputs and outputs go through guardrails. Least privilege is enforced at the tool level, so even a manipulated agent can’t do much damage. And high-impact actions need human approval.

It just means being able to see what your agent did: which tools it called and why, what it cost, and whether its answers stayed accurate. Without it, problems with the agent are not visible until they have been discovered by a customer. So we build tracing and evaluation into every agent, using tools like LangSmith, RAGAS, and DeepEval, and we keep tracking behavior, cost, and latency after launch.

Orchestration is how you get multiple agents, tools, and data sources working together without stepping on each other. Here’s an example. On a subscription billing platform, we set up seven read-only agents under one central agent that figures out what’s being asked and routes it. A person completes every transaction. This setup reached 91% accuracy in selecting the right tool.

The main ways costs increase are context bloat — using too much context in prompts to the model — using unnecessary model calls, and using a larger model than needed to perform a task. We mitigate this by building cost-conscious agents from the start. For a B2B marketing platform, we pre-organized the database schema instead of stuffing it into prompts, and that saved about 6,000 tokens per query. After launch, we track cost continuously and send a weekly drift and cost report.