Autonomous agents that research, decide, and act on your behalf. From lead qualification to invoice processing, agents that think, not just respond. We build agents that run 24/7 without supervision.
We define the agent's goal, decision boundaries, available tools, and escalation rules, so it acts autonomously within safe limits.
We architect the agent's memory system, short-term context, long-term vector storage, and retrieval strategy for accurate, consistent behavior.
We connect the agent to your APIs, databases, and SaaS tools. The agent can read, write, and trigger actions across your entire stack.
We run adversarial tests, measure task completion rates, and deploy safety guardrails, so the agent performs reliably in production.
How is a custom AI agent different from a chatbot?
A chatbot responds to messages. An AI agent pursues goals across multiple steps, calling APIs, reading databases, making decisions, and triggering actions autonomously. It completes tasks rather than answering questions.
How long does it take to build a custom AI agent?
A well-scoped single-domain agent takes 6–10 weeks from kickoff to production. This includes prompt engineering, tool integration, edge-case testing, and shadow-mode validation before full deployment.
Which AI models do you use?
Primarily GPT-4o and Claude 3.5 Sonnet for reasoning tasks. We evaluate models against your specific use case, balancing accuracy, latency, and cost, and give you full control over the model choice.
How do you handle failures and unexpected inputs?
Every agent has a human-in-the-loop escalation path, confidence thresholds, and full audit logging. When something falls outside expected parameters, it routes to a human rather than acting with false confidence.
Core capabilities delivered in every Custom AI Agents engagement.
Agents plan through tasks using the Reason-Act-Observe loop, deciding their next action based on what previous steps returned. This allows reliable handling of multi-step tasks with multiple valid execution paths depending on intermediate data.
Long-term memory stored in Pinecone or pgvector gives agents the ability to recall past interactions, maintain customer context across sessions, and access relevant domain knowledge without injecting an entire knowledge base into every prompt.
Agents call real APIs, query databases, send emails, update CRM records, browse websites, and execute code as individual tool calls within a single task run. We define tools with strict schemas to ensure reliable argument construction at inference time.
Every agent has defined confidence thresholds and escalation conditions. When a task falls outside expected parameters, the agent surfaces its current state to a human reviewer via Slack or email rather than proceeding with false confidence.
Complex workflows run multiple specialized agents in sequence or parallel. An orchestrator agent decomposes the goal and delegates sub-tasks to domain-specific sub-agents, each with its own tool set, instructions, and memory scope.
Before full production deployment, agents run alongside existing human processes, logging what they would have done without executing. This catches edge cases and builds stakeholder confidence before the automation goes fully live.
We work with teams across these functions and industries.
Companies that need 24-hour lead qualification, prospect research, and CRM enrichment without expanding headcount. Agents qualify inbound leads against your ICP, draft first-touch outreach, and keep CRM records current automatically.
Organizations processing invoices, purchase orders, and expense reports manually. AI agents read documents, extract structured data, validate against policies, route for approval, and post to accounting systems in seconds, not hours.
SaaS companies and service businesses with high support volume. Agents resolve tier-one tickets, identify churn signals, draft responses for human review, and route complex cases to the right team without manual triage.
Medical organizations running prior authorization, clinical documentation review, or research data extraction at scale. Agents handle the structured, repeatable portions of these workflows while clinical staff review edge cases.
Lead qualification time reduced from 25 minutes per lead to under 90 seconds with AI enrichment and scoring
Prior authorization agents process 10x more requests per staff member without accuracy degradation
Support ticket resolution for tier-one cases typically reaches 65–75% without human intervention
CRM data completeness improves from under 60% to 95%+ when agents handle post-call enrichment automatically
A custom AI agent is an autonomous software system that receives a goal, plans a sequence of actions to achieve it, calls external tools and APIs as needed, evaluates intermediate results, and adapts its approach when earlier steps do not go as expected — all without requiring a human at each decision point. Unlike a chatbot that responds to messages, an agent completes tasks. The distinction matters because most business workflows involve multiple steps, multiple systems, and decision points where the right next action depends on what just happened.
The architecture of a production AI agent is more complex than a single LLM call. At the core is a reasoning layer — typically GPT-4o or Claude — that receives a task description, a list of available tools, and the history of what the agent has done so far. It produces a structured output that specifies the next action. A tool execution layer carries out that action — querying a database, calling an API, sending a message, or updating a record. An orchestration layer manages state, logs every action, and handles retries when tools fail. A human escalation path catches cases where the agent's confidence falls below a defined threshold.
The workflows where custom AI agents deliver the clearest ROI are those with high volume, repeatable decision logic, and significant cost per manual decision. Lead qualification, prior authorization, invoice processing, support ticket triage, and compliance document review all share these characteristics. The agent does not need to be creative — it needs to be consistent, fast, and reliable across thousands of instances of the same decision pattern.
Kodesinc builds custom AI agents for clients across healthcare, financial services, sales operations, and enterprise back-office functions. Every agent we deploy includes full audit logging of every decision and action, a human-in-the-loop escalation path for edge cases, and shadow-mode validation before production deployment. Clients own all agent code, prompts, and configurations at handover. No vendor lock-in to our infrastructure or tooling.
Memory architecture is one of the more underappreciated design decisions in AI agent development. A stateless agent that receives no context about past interactions will make inconsistent decisions, fail to recognize returning customers, and repeat clarifying questions it has already asked. Production agents need a tiered memory model: an in-context window for the current task, a session store for the current conversation thread, and a long-term vector memory for facts that should persist across interactions. We use Pinecone, Weaviate, or pgvector depending on the scale and infrastructure constraints of the deployment, and we design the retrieval strategy around what the agent needs to recall and how quickly it needs to recall it.
Evaluation frameworks for AI agents require a different approach than standard software testing. An agent that completes 95% of tasks successfully but fails on a specific category of edge cases may be harder to debug than a conventional system bug, because the failure mode is probabilistic rather than deterministic. We build evaluation suites that run agents against hundreds of representative task scenarios with known expected outcomes, measure task completion rate by category, and flag regressions when prompt changes affect performance. This infrastructure is what allows you to improve agent behavior over time rather than shipping changes and hoping nothing breaks.
The handover process at the end of an agent development engagement is as important as the build itself. A custom AI agent that nobody on your team understands is an operational liability. We deliver a plain-language architecture document describing every component of the agent and its decision logic, annotated prompt templates with commentary explaining the reasoning behind each design choice, runbooks for the most common operational tasks including monitoring, restarting, and debugging failed executions, and a post-launch support period during which we are available to assist with issues as your team builds confidence operating the system independently.