AI Agents as a Service
Most organisations have already tried AI chat tools. The next step is more powerful.
AI agents are software workers that can understand a goal, plan the steps, use tools, access approved systems, complete tasks, ask for human approval when needed, and keep improving over time.
At Techwondoe, we help enterprises design, build, deploy and operate AI agents that are connected to real business workflows. These agents can work across your internal systems, documents, APIs, CRMs, ERPs, ticketing tools, finance systems, project tools and customer platforms.
The result is not another chatbot. It is a practical, secure and measurable way to automate high-value work.
We provide an end-to-end AI Agents as a Service offering.
This includes strategy, use-case discovery, architecture, prototype development, enterprise integration, governance, deployment, monitoring, support and ongoing improvement.
Our focus is on building agents that are useful in production, safe to operate, easy for teams to adopt, and continuously improved as your business and AI models evolve.
An AI agent that can understand customer requests, search knowledge bases, check account information, suggest responses, create tickets, escalate complex cases and update CRM records.
Example use case:
A customer asks about an order, billing issue or product problem. The agent checks internal systems, drafts a response, creates a support ticket if needed, and passes the case to a human when confidence is low.
An AI agent that helps sales teams prepare for prospects and client meetings.
Example use case:
The agent researches a target company, summarises recent news, identifies likely pain points, reviews CRM history, prepares discovery questions and drafts a follow-up email after the meeting.
An AI agent that assists with invoice processing, payment follow-up, reconciliation and financial administration.
Example use case:
The agent reads incoming invoices, extracts key data, checks purchase order details, flags mismatches, prepares approval notes and updates finance workflows.
An AI agent that helps staff find answers across internal documents, policies, project notes, proposals, architecture documents and client history.
Example use case:
A team member asks, “Have we done a similar AWS migration before?” The agent searches approved internal sources, summarises relevant examples, links to source documents and suggests reusable patterns.
An AI agent that supports product and engineering teams across the software delivery lifecycle.
Example use case:
The agent reviews requirements, creates draft user stories, checks acceptance criteria, maps dependencies, generates test scenarios, summarises pull requests and helps prepare release notes.
An AI agent that reviews work against internal policies, security requirements, privacy rules and compliance checklists.
Example use case:
Before a feature is released, the agent checks whether logging, access control, data retention, consent, audit trails and privacy requirements have been considered.
An AI agent that assists operational teams working across locations, assets, hardware devices or field workflows.
Example use case:
The agent receives a field issue, checks previous maintenance history, suggests troubleshooting steps, creates a work order and alerts the right person if the issue is urgent.
We start by identifying the workflows where AI agents can create real business value.
We look for work that is repetitive, knowledge-heavy, rules-driven, document-heavy, integration-heavy or slow because it depends on multiple systems and handovers.
Typical outputs:
We build a working prototype for one high-value workflow.
This is not a slide deck or concept demo. It is a functional agent that can use approved data, follow a defined process and show how the experience will work.
Typical outputs:
Once the prototype is validated, we build the production version.
This includes the agent backend, user interface, APIs, integrations, monitoring, authentication, access control and deployment pipeline.
Typical outputs:
AI agents need to be deployed carefully because they often interact with business systems, sensitive data and live workflows.
We use a controlled deployment process that reduces risk and allows the organisation to build confidence before broad rollout.
Typical deployment stages:
Before go-live, we validate:
This makes the agent safe to launch and easier to support.
Once the agent is live, we keep it running as an ongoing managed service.
This is important because AI agents are not static software. Models change, business rules change, documents change, APIs change, workflows change, and users find new ways to use the agent.
Our managed service keeps the agent reliable, secure and useful over time.
The managed service can include:
We operate AI agents using a structured AgentOps process.
AgentOps is the ongoing operational layer around AI agents. It makes sure the agent continues to perform well in production and does not become outdated, unreliable or unsafe.
We monitor the agent across technical, business and AI-specific dimensions.
This can include:
This gives both the client and Techwondoe visibility into how the agent is performing.
For enterprise use, every important action should be traceable.
Depending on the use case, we can log:
This helps with debugging, compliance, risk management and continuous improvement.
Not every action should be fully automated.
For sensitive or high-impact workflows, the agent can be configured to ask for human approval before taking action.
Examples:
This allows the organisation to benefit from automation while still keeping control.
If something goes wrong, we follow a defined support and escalation process.
Examples of incidents:
Our response can include triage, rollback, disabling a specific tool, switching to a fallback model, updating prompts, fixing an integration, or temporarily requiring human approval for affected workflows.
AI agents can create cost surprises if usage is not monitored properly.
We help manage this through:
The goal is to keep the agent useful without allowing cost to grow uncontrolled.
AI agents need regular updates across several layers.
When business rules or workflows change, the agent needs to reflect those changes.
Examples:
We update the agent workflow, instructions, rules and approval logic to match the latest business process.
Many agents rely on internal documents, policies, FAQs, proposals, technical docs or customer information.
We keep this current by setting up a managed knowledge refresh process.
This can include:
This helps reduce the risk of the agent using outdated or incorrect information.
AI models improve quickly, but model changes can also affect behaviour.
We do not blindly upgrade production agents.
Before changing a model, we test the agent against a set of expected scenarios and compare quality, cost, speed and reliability.
Model updates can include:
Prompts are part of the agent’s operating logic.
We maintain and improve prompts based on real usage, user feedback, errors and new business requirements.
Prompt updates can include:
For production use, prompt updates should be versioned, tested and released like software changes.
Agents often rely on APIs and business systems.
When these systems change, the agent needs to be updated too.
Examples:
We maintain the integration layer so the agent continues to work reliably as the surrounding systems evolve.
Before releasing important changes, we test the agent against known scenarios.
This can include:
This gives confidence that updates improve the agent without breaking existing behaviour.
We use a controlled release process for production agents.
A change may come from user feedback, business process changes, model updates, cost optimisation, a new integration, or an incident.
We assess the expected benefit, risk, affected workflows, data access implications and testing required.
We update the relevant layer. This may be prompts, workflows, tools, APIs, model configuration, retrieval settings, UI or infrastructure.
We test the change in a non-production environment using predefined evaluation scenarios.
For business-critical changes, the client reviews and approves the behaviour before production release.
The change is deployed through a controlled release process.
Depending on the risk, this can be released to all users, a pilot group, or behind a feature flag.
After release, we monitor performance, errors, cost, user feedback and business outcomes.
New ideas, issues and optimisations are added to a managed backlog and prioritised with the client.
A typical enterprise AI agent solution includes:
We can build on your existing cloud and AI stack, or help you choose the right one.
AI agents are especially useful when a workflow involves:
They are less suitable where the process is unclear, data access is poor, risk is too high, or the business is not ready to define ownership and controls.
We are not just AI experimenters. We are software engineers, cloud architects and product delivery specialists.
That matters because useful AI agents need more than prompts. They need secure architecture, clean integrations, robust APIs, reliable infrastructure, testing, monitoring, governance and ongoing support.
We bring together:
Best for organisations that know they want to explore AI agents but need clarity on where to start.
Duration: 1 to 2 weeks
Outcome: prioritised agent roadmap, first use-case definition and implementation plan.
Best for validating one high-value workflow quickly.
Duration: 2 to 4 weeks
Outcome: working prototype connected to sample or approved business data.
Best for organisations ready to deploy an agent into a real workflow.
Duration: 6 to 12 weeks depending on complexity
Outcome: production-ready AI agent with integrations, governance, monitoring and support.
Best for organisations that want ongoing operation, support and improvement.
Duration: monthly retainer
Outcome: monitoring, optimisation, support, cost control, prompt improvements, model upgrades, knowledge refreshes, integration maintenance and new workflow enhancements.
For low-risk internal agents.
Includes:
For agents used regularly by teams or departments.
Includes:
For business-critical agents used across departments or customer-facing workflows.
Includes:
Automates first-level customer support, ticket triage, knowledge lookup and response drafting.
Gives staff a trusted way to search policies, proposals, project documents and internal knowledge.
Prepares prospect research, meeting notes, CRM updates, follow-up emails and account insights.
Supports requirements review, user story creation, test scenario generation, release notes and delivery reporting.
Assists with invoice processing, reconciliation, payment follow-ups and approval workflows.
Our goal is simple.
We help enterprises move from AI experiments to AI-powered operations.
We build agents that are useful, secure, measurable and connected to the way your business actually works.
Then we keep them running, improving and adapting as your business changes.