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AI AGENT DEVELOPMENT
Build AI agents that act with appropriate control.
CoderLyft designs and develops multi-step AI agents that can use approved tools, follow defined permissions, and escalate to people when needed. Move from a workflow opportunity to agent behaviour that fits your systems, risk profile, and operational oversight requirements.
Tool integration • Workflow automation • Human approval • Monitoring
1. Request
- User intent
- Channel context
- Account scope
- Priority
2. Plan and tools
- Task breakdown
- Tool selection
- Data retrieval
- Permission check
3. Review
- Proposed action
- Source checks
- Human approval
- Exception flag
4. Controlled action
- System update
- Recorded outcome
- Audit trail
- Follow-up task
An OpenAI Select Partner
As part of the OpenAI Partner Network, CoderLyft helps organisations explore, build, deploy, and scale AI agent solutions responsibly and effectively.
Learn about our AI capabilities
FROM REQUEST TO CONTROLLED ACTION
An agent is only useful when its actions are bounded.
The value of an AI agent appears when it can interpret a request, use the right tools, follow business rules, and know when to stop for human review—not when it can act without limits.
Defined objectives
Start with the workflow the agent should support, the outcomes that matter, and the actions that require explicit permission or approval.
Approved tools and context
Connect agents to authorised systems, data, and knowledge sources instead of granting broad or undefined access.
Human oversight
Design escalation paths, review points, logging, and responsibility so consequential actions remain controlled.
WHAT WE CAN BUILD
Agent capabilities designed for real workflows.
Each engagement should begin with the business process, permitted actions, integration boundaries, and risk—not with an agent framework selected in advance.
Begin with the workflow and permitted actions.
We help define where an agent may create practical value, which tools it may use, what information it requires, and which steps must remain with authorised people.
What we can help define:
- Business objective and success criteria.
- Users, triggers, and workflow responsibilities.
- Permitted and restricted actions.
- Integration and data dependencies.
- Approval, escalation, and audit requirements.
- Prototype and delivery scope.
Connect agents to approved systems and APIs.
Agents should interact with business systems through defined interfaces, credentials, and permissions—not through unrestricted access.
What we can build:
- CRM, ERP, and service-platform integrations.
- Internal APIs and webhook workflows.
- Document and record lookup tools.
- Communication and notification actions.
- Scoped credentials and access controls.
- Tool validation and error handling.
Coordinate multi-step agent behaviour.
Design how an agent interprets requests, selects tools, handles intermediate results, and progresses through a workflow with clear stopping conditions.
What we can build:
- Request triage and routing logic.
- Multi-step task planning and execution.
- Conditional branching and retries.
- Queue and asynchronous processing.
- State management across steps.
- Timeout, fallback, and exception paths.
Keep consequential actions under review.
Agents can prepare actions, drafts, and recommendations, but sensitive or high-impact steps should follow defined approval and authorisation rules.
What we can build:
- Approval gates before external actions.
- Draft-and-review workflows.
- Role-based authorisation checks.
- Escalation to a person or team.
- Rejection, revision, and retry handling.
- Audit trails for reviewed decisions.
Give agents the context they need—no more.
Agents work best with relevant session context, approved knowledge, and structured records scoped to the task and user—not with unrestricted historical access.
What we can build:
- Session and conversation context.
- Retrieval from approved knowledge sources.
- Structured record and entity context.
- Permission-aware information access.
- Context summarisation and pruning.
- Source references where appropriate.
AGENT ARCHITECTURE
Connect models, tools, policies, and people.
A production agent requires more than a model with function calling. It needs orchestration, permissions, monitoring, evaluation, and a clear path for human intervention.
Example agent architecture
1
Users and channels
Web applications • Mobile applications • Employee workspaces • Chat and authorised voice channels
2
AI experiences
Assistant • Copilot • Agent workspace • Search and knowledge experience
3
Orchestration and controls
Prompt and workflow management • Retrieval • Model routing • Tool permissions • Validation • Human approval • Guardrails and escalation
4
Models, knowledge, and systems
Approved AI models • Enterprise knowledge • Databases • CRM and ERP • eCommerce and business applications • External APIs
5
Operations
Logs and activity history • Evaluation • Monitoring • User feedback • Error and exception handling • Cost and usage visibility
Architecture, hosting, model, tool, and data choices depend on the use case, security requirements, system access, expected workload, and available infrastructure.
Measure agent behaviour before expanding responsibility.
Agent quality should be evaluated against intended tasks, representative inputs, tool-use scenarios, and the consequences of an incorrect or unauthorised action.
What we can implement:
- Use-case-specific evaluation sets.
- Tool selection and execution validation.
- Workflow completion and failure testing.
- Approval and escalation path checks.
- Error, timeout, and exception monitoring.
- User feedback collection.
- Controlled prompt, tool, and workflow updates.
WHERE AGENTS CAN HELP
Explore agent opportunities across the business.
Customer support
- Business situation
- Support teams handle varied requests that require lookup, drafting, routing, and sometimes account or policy actions.
- Potential AI capability
- Agents can classify requests, retrieve relevant knowledge, prepare responses, and route or escalate work while keeping consequential actions controlled.
- Systems or information
- Help desk, CRM, knowledge base, communication channels.
Control: Refunds, account changes, contractual commitments, and payment actions require appropriate authorisation.
- Request triage and routing
- Knowledge-backed response drafting
- Case summarisation
- Escalation preparation
- Follow-up task creation
Sales operations
- Business situation
- Sales teams need timely qualification, research, and CRM updates while preparing proposals and follow-ups.
- Potential AI capability
- Agents can assist with lead research, CRM record preparation, and next-step recommendations without replacing system authority.
- Systems or information
- CRM, product catalogue, pricing systems, email and calendar tools.
Control: Pricing, discounts, availability, and customer-account changes must follow defined permissions and approval rules.
- Lead qualification support
- Account and contact research
- CRM update preparation
- Meeting and follow-up drafting
- Pipeline task coordination
Internal operations
- Business situation
- Operational teams coordinate requests, documents, and exceptions across disconnected internal systems.
- Potential AI capability
- Agents can prepare routing, summaries, data lookups, and next actions while preserving approval rules for high-impact steps.
- Systems or information
- ERP, document stores, service platforms, internal queues.
Control: Operational changes, vendor actions, and financial steps should follow defined permissions and review paths.
- Request intake and routing
- Document processing preparation
- Exception summarisation
- Approval packet assembly
- Status update coordination
Knowledge work
- Business situation
- Teams need to research, synthesise, and act on information spread across documents, systems, and project records.
- Potential AI capability
- Agents can retrieve approved material, prepare structured summaries, and initiate follow-up tasks with source references and scoped access.
- Systems or information
- Document repositories, intranet, project tools, internal databases.
Control: Access should follow existing knowledge and document permissions; external sharing requires review where appropriate.
- Research and synthesis support
- Policy and process lookup
- Brief and summary preparation
- Task creation from findings
- Answers with source references
Product automation
- Business situation
- Product teams want agent behaviour embedded in applications users already rely on.
- Potential AI capability
- In-product agents can assist with user tasks, tool use, and workflow steps designed around user context and monitored behaviour.
- Systems or information
- Application APIs, user accounts, analytics, feature flags.
Control: Product actions should be evaluated and monitored within the intended user context and permission model.
- Embedded task assistants
- In-app workflow copilots
- User-triggered automation
- Context-aware tool use
- Guided multi-step product flows
ENGINEERING FOUNDATION
Technology selected around the agent problem.
Application and orchestration
Laravel and PHP, Python, Node.js, and workflow orchestration patterns suited to multi-step agent execution.
Models and agent services
Authorised commercial or open-source models and agent frameworks selected according to capability, control, cost, and deployment requirements.
Tools and retrieval
API integrations, function calling, structured tools, vector retrieval, search, and permission-aware knowledge access.
Business integration
REST APIs, webhooks, queues, CRM, ERP, service platforms, and custom systems connected through scoped interfaces.
Operations
Project-appropriate cloud infrastructure, access control, logging, monitoring, evaluation, and release workflows.
HOW WE DELIVER
From workflow opportunity to operational agent.
-
01
Discover
Clarify the business outcome, users, workflow, permitted actions, data, constraints, and measurement approach.
Typical outputs: Prioritised use case • Workflow and action definition • Tool and integration review • Initial risk and feasibility assessment
-
02
Architect
Define the agent design, tool boundaries, orchestration, permissions, approval paths, and evaluation approach.
Typical outputs: Agent architecture • Tool and integration plan • Control and escalation design • Evaluation approach
-
03
Build
Develop the agent orchestration, tool integrations, review interfaces, and operational components.
Typical outputs: Working agent increments • Connected tools and workflows • Review and administration tools • Technical documentation
-
04
Validate
Test agent behaviour against intended tasks, representative inputs, tool scenarios, failure conditions, and user expectations.
Typical outputs: Evaluation results • Workflow and tool testing • Identified limitations • Release recommendations
-
05
Deploy
Release within the agreed environment, monitor behaviour, gather feedback, and improve deliberately.
Typical outputs: Production release • Monitoring and support approach • Feedback process • Controlled improvement roadmap
RESPONSIBLE DELIVERY
Controls designed into the agent.
Bounded actions
Define what the agent may prepare, recommend, or execute—and which actions remain with authorised people.
Scoped access
Grant tools and data access appropriate to the task, user, and environment—not broad system privileges.
Visible limitations
Design for uncertainty, tool failures, missing information, exceptions, and escalation.
Traceable operation
Retain appropriate visibility into requests, tool use, approvals, outcomes, feedback, and system behaviour.
ILLUSTRATIVE AGENT WORKFLOW
From a service request to an approved action.
- 1 A user submits a service request through an approved channel.
- 2 The agent retrieves relevant account and policy context.
- 3 The agent selects permitted tools to investigate and prepare a response.
- 4 Missing, uncertain, or high-impact information is flagged for review.
- 5 A person approves, revises, or rejects the proposed action.
- 6 The outcome, tool use, and approval decision are recorded.
An illustrative agent workflow CoderLyft could design; not a published client result.
Frequently Asked Questions
Can CoderLyft build AI agents that work with our existing systems?
Yes. Agents can be scoped around available APIs, data access, permissions, infrastructure, and business requirements. Tool integration and approval design are part of the engagement.
How do you decide which actions an agent can take?
Discovery and architecture define permitted tools, restricted actions, approval gates, and escalation paths based on the workflow, risk, and operational responsibility.
Can agents operate without human review?
Some low-risk preparatory steps may run automatically, but consequential actions should follow defined review, authorisation, or approval rules appropriate to the use case.
Do we need a prototype before production deployment?
A focused prototype can test feasibility and workflow value, but production deployment requires additional work around integration, security, evaluation, monitoring, and operations.
Can agents use our internal documents and knowledge?
Yes, where documents can be accessed appropriately. The design should consider permissions, document currency, retrieval quality, source references, and user responsibilities.
Which model or agent framework will you use?
The model and orchestration approach should be selected after reviewing the task, tool requirements, quality needs, privacy constraints, deployment preferences, workload, and cost. The architecture should avoid unnecessary dependence on one provider where flexibility matters.