SMART INSIGHTS / AI IMPROVEMENT ENGINE
Dashboards tell support teams what happened. Smart Insights should show what needs to change.
Every production conversation contains evidence about where an AI support agent is working, where it is failing, and what would make it better. The hard part is not collecting more transcripts. The hard part is turning thousands of calls, chats, transfers, failed resolutions, customer corrections, and repeated edge cases into specific improvements that can be validated, approved, deployed, and measured.
Giga Smart Insights is the improvement layer for support agents. It surfaces recurring patterns, uncovers root causes, ties them to the business metric the team cares about, and turns them into concrete recommendations: a policy modification, a new fallback flow, a missing API, a knowledge-base repair, a deterministic action, or a conversational design change.
Core insight Improvement > Reporting. A dashboard can show that resolution rate dropped. An improvement engine should explain which conversations caused the drop, what the agent was missing, which policy or workflow should change, and how that change should be validated before it becomes production behavior. |
Surface recurring support patterns from live agent interactions.
Tie improvement opportunities to selected KPIs such as resolution rate, escalation rate, containment, customer satisfaction, revenue, or retention.
Validate hypotheses against transcript evidence instead of relying on anecdotal QA review.
Recommend policy updates, fallback flows, knowledge repairs, tool/API additions, and workflow changes.
Keep humans in the approval loop before agent behavior changes in production.
Primary CTA: See what your conversations are trying to fix. Secondary CTA: Bring one KPI and a week of transcripts.
Why support teams need an improvement engine
Customer support has always produced more conversation data than teams can use. The difference with AI agents is that the conversation data is no longer just a record of what happened. It is the training ground for what the agent should become next.
A support leader can usually see the symptoms. Transfers are rising. Customers abandon calls after a failed verification step. A delivery issue keeps escalating. A billing question keeps producing partial answers. Human agents are still needed for the same narrow category of work. The dashboard can show that these problems exist, but it rarely explains the operational gap behind them.
The useful question is not only “what happened?” The useful question is: what is the gap between the agent’s current behavior and the behavior required to reach the metric the business agreed to improve? That gap may be missing data access. It may be a policy conflict. It may be a workflow the agent is not allowed to execute. It may be a bad fallback. It may be a knowledge-base hole. It may be conversational design: the agent is asking a question in a way that confuses customers or fails to handle how people actually describe the problem.
Smart Insights should make that gap visible. It should not stop at analytics. It should turn production behavior into a ranked improvement backlog for the agent.
Why the obvious fix fails: dashboards and manual QA
Common fix | Where it helps | Where it fails |
Traditional dashboard | Shows aggregate metrics such as volume, resolution, transfers, sentiment, and handle time. | Does not explain which repeated interaction pattern caused the metric to move or what should change in the agent. |
Manual QA sampling | Gives reviewers a close reading of selected conversations. | Samples too little, arrives too late, and can miss repeated failure modes outside the reviewed set. |
Static ticket tagging | Adds structure to support records. | Depends on pre-existing categories. It struggles with new patterns, multi-intent conversations, and root-cause hypotheses. |
Generic transcript summarization | Makes individual calls easier to skim. | Summaries compress the evidence but do not necessarily produce an improvement path. |
One-off prompt edits | Can fix a visible failure fast. | Without transcript-backed validation, the edit may solve one problem while creating new failure modes elsewhere. |
An improvement engine needs a different posture. It has to treat conversations as evidence, not just records. It has to know the KPI the customer wants to improve. It has to compare live behavior against target behavior. It has to distinguish a noisy one-off from a repeated operational gap. It has to recommend the smallest useful system change, then keep measuring whether the change worked.
Definition
What is an AI improvement engine for support agents? An AI improvement engine is a system that analyzes production support conversations, identifies repeated gaps between agent behavior and desired business outcomes, validates those gaps against transcript evidence, and recommends concrete changes to policies, workflows, knowledge, tools, or conversation design so the agent improves over time. |
For Giga, Smart Insights is not simply a reporting tab. It is the system that helps the agent learn from the work it has already done without turning production customers into an uncontrolled test harness.
The Smart Insights operating loop
The system is a loop that moves from production evidence to validated change.
Step | What happens | Why it matters |
1. Choose an objective | The team selects a KPI or outcome such as resolution rate, escalation rate, containment, CSAT, revenue recovery, or retention risk. | The system is not looking for “interesting” patterns in the abstract. It is looking for changes that matter to the customer’s agreed success metric. |
2. Collect conversation evidence | The engine analyzes transcripts, outcomes, tags, sentiment, agent actions, transfers, abandonment, custom fields, and journey signals. | A support issue is rarely visible in one field. The evidence usually spans what the customer asked, what the agent did, what system data was available, and how the conversation ended. |
3. Cluster repeated failure patterns | Conversations are grouped by shared behavior: failed verification, repeated transfer, missing status lookup, unresolved billing detail, unsupported cancellation flow, or escalation trigger. | The goal is to separate recurring operational gaps from one-off noise. |
4. Identify the likely root cause | The system asks what prevented the agent from resolving the interaction: missing API, policy ambiguity, knowledge gap, tool limitation, conversational design issue, hallucination risk, or handoff rule. | A cluster is not enough. The useful output is the reason the cluster exists. |
5. Recommend a system change | Smart Insights proposes an improvement such as a policy modification, fallback workflow, knowledge repair, deterministic tool action, API request, or escalation rule change. | The recommendation must be implementable. Otherwise the insight becomes another dashboard artifact. |
6. Validate, approve, deploy, measure | Humans review transcript evidence and projected impact, approve controlled updates, then monitor the selected KPI after deployment. | The loop only closes when the team can see whether the change improved production behavior. |
The system should optimize for the customer’s chosen definition of better
The improvement process is shaped by what the customer cares about and what the customer has explicitly agreed to be evaluated on. Smart Insights should not imply that every pattern is equally important. A pattern matters when it changes the metric the customer wants to improve, exposes a risk the customer needs to control, or reveals a capability the agent must develop to reach production reliability.
Smart Insights is not a generic pattern finder. It is a KPI-aware improvement engine.
Outcome states
Not every discovered pattern should become an automatic change. A serious enterprise system needs clear outcome states.
Outcome | What the system found | What happens next |
Outcome 1: Clear policy update | A repeated failure maps cleanly to a policy gap or fallback rule. Example: customers asking for an operator are forced through three open-ended clarification turns before transfer. | Smart Insights recommends a policy modification, shows transcript evidence, estimates KPI impact, and routes the change to human review. |
Outcome 2: Missing tool or API | The agent transfers because it lacks read/write access to a business system. Example: delivery drivers ask how much they have made so far, but the agent cannot access earnings data. | The recommendation becomes a capability request: expose an API, add a tool, create a deterministic code block, or update the workflow boundary. |
Outcome 3: Knowledge repair | The same question appears repeatedly and the agent cannot answer with enough certainty. | Smart Insights proposes a knowledge-base addition, FAQ rule, or retrieval update, then validates against transcripts before deployment. |
Outcome 4: Conversational design issue | Customers express the same problem in varied language and the agent does not ask the right clarifying question. | The recommendation changes the interaction path, not only the facts. The system may suggest troubleshooting steps, clarification sequencing, or tone changes. |
Outcome 5: Needs human review | The pattern exists, but the root cause or safe update is ambiguous. | The system keeps the recommendation in review, surfaces evidence, and prevents automatic deployment. |
Outcome 6: False or low-value pattern | The cluster is statistically visible but not operationally meaningful, or it does not affect the selected KPI. | The system deprioritizes it, keeps the evidence available, and avoids generating work for the team. |
Failure taxonomy: what Smart Insights should look for
Technical buyers trust products that name their failure categories. A Smart Insights page should not say “we uncover patterns” and stop there. It should show the types of patterns the system is built to find.
Failure type | What it looks like in transcripts | Likely improvement |
Missing read access | The customer asks for account, order, payout, availability, subscription, claim, or delivery data the agent cannot retrieve. | Expose read API, add tool call, create lookup flow, or route to browser/action system. |
Missing write access | The agent knows the answer but cannot complete the action: update address, book appointment, cancel order, issue credit, change reservation. | Add write API, deterministic code block, approval rule, or human-in-the-loop action. |
Policy ambiguity | The agent hesitates, escalates, or gives inconsistent answers when rules conflict or are under-specified. | Rewrite policy, add decision tree, define thresholds, clarify exceptions. |
Knowledge gap | Repeated questions are unanswered, answered partially, or resolved only after human intervention. | Add knowledge article, FAQ handling rule, retrieval update, or market-specific guidance. |
Fallback failure | The agent retries the same broken flow, asks redundant questions, or transfers too late. | Add fallback workflow, failure-specific handoff, guided menu, or retry mechanism. |
Conversation design gap | Customers describe the same issue differently and the agent fails to map those variations to the right next step. | Add examples, troubleshooting prompts, clarification strategy, or subintent handling. |
Hallucination risk | The agent invents a policy, contradicts instructions, or contradicts earlier conversation context. | Strengthen guardrails, use hallucination correction, repair knowledge, and add validation criteria. |
Compliance or redaction gap | Sensitive data appears in transcripts or interactions lack required metadata. | Add redaction, compliance tags, audit alerts, or restricted action logic. |
Market/language-specific friction | Resolution drops or escalations rise in a particular region, language, or accent group. | Adjust language coverage, routing, localized knowledge, escalation, or phrase handling. |
What makes the engine different from conventional contact center analytics
Most contact center analytics products are optimized for visibility. Smart Insights should be positioned around change. The distinction matters because AI agents can be updated directly: policies can change, tools can be added, knowledge can be repaired, workflows can be re-routed, and deterministic actions can be inserted where open-ended reasoning is too risky.
Analytics layer | Improvement engine | Why the distinction matters |
Reports transfer rate. | Identifies which transfer cluster is caused by missing data access. | The second version points to an implementable fix. |
Shows unhappy sentiment. | Finds the workflow step that causes sentiment deterioration. | The team can change the policy or fallback before more conversations fail. |
Tags tickets after the fact. | Discovers new subintents and failure patterns across transcripts. | The taxonomy can evolve as customers reveal new problems. |
Summarizes conversations. | Converts conversations into policy, tool, and knowledge recommendations. | The output becomes agent work, not only manager visibility. |
Measures the past. | Tests whether the next update moved the chosen KPI. | The system closes the loop. |
Example workflow: from transfer cluster to agent update
A useful subfeature page needs a concrete workflow. This example should be adapted to an approved customer use case before publication.
Scenario: repeated human transfer for payout questions
A delivery marketplace deploys a Giga voice agent for driver support. The selected KPI is containment rate, with a secondary goal of maintaining resolution quality. After launch, Smart Insights sees a repeated pattern: drivers ask how much money they have earned so far, and the agent transfers them to a human operator.
System stage | Evidence | Improvement path |
Pattern surfaced | A cluster of transcripts contains payout questions followed by human transfer. | Flag as an improvement opportunity tied to containment rate. |
Root cause hypothesized | The agent does not have access to the driver’s earnings data. | Classify as missing read access rather than a prompt-quality issue. |
Recommendation generated | Expose an earnings lookup API or tool so the agent can retrieve the value safely. | Route to product/engineering or FDE workflow. |
Policy and guardrails defined | The agent can read payout amount but cannot make payout-policy promises beyond the retrieved data. | Add action boundary and hallucination-sensitive guardrails. |
Deployment controlled | The new tool is tested on payout-question transcripts before full rollout. | Launch to a controlled slice, then monitor containment and escalation. |
Impact measured | Transfer rate for payout questions declines while resolution quality remains acceptable. | Promote the change and continue monitoring for downstream issues. |
How Smart Insights connects to the rest of Giga
Smart Insights is strongest when presented as the improvement layer across Giga’s agent platform, not an isolated analytics product.
Giga layer | Relationship to Smart Insights | What gets better |
Agent Canvas | Policies, workflows, logic, and training materials are where many Smart Insights recommendations should land. | Agent behavior becomes easier to govern, test, and update. |
Voice Experience | Voice calls produce high-signal evidence: interruptions, sentiment shifts, unresolved turns, transfers, abandonment, and latency-sensitive failures. | Voice agents improve from production calls without relying only on human QA. |
Browser Agent / tools | Some insights reveal missing execution paths inside browser systems, internal tools, or APIs. | Agents can move from answering to acting. |
Hallucination correction | Caught hallucinations and correction events can become labeled examples for policy repair, knowledge auditing, and guardrail improvement. | Accuracy work becomes part of the broader improvement loop. |
Console review workflow | Smart suggestions should be validated, approved, and deployed with human oversight. | Enterprise teams keep control over agent behavior. |
Governance: improvements should not deploy themselves
Smart Insights can surface a policy update, but enterprise buyers need to know how changes are reviewed, staged, and rolled out.
Recommended governance model
Define the metric and allowed improvement categories before running the insight.
Require transcript evidence for any suggested policy or workflow update.
Classify the recommendation as policy, knowledge, tool/API, fallback, escalation, compliance, or conversation design.
Estimate the affected conversation volume and expected direction of KPI movement.
Route the recommendation to the correct owner: support ops, product, engineering, compliance, FDE, or CX leadership.
Test the update against historical transcripts and simulated edge cases.
Deploy to a controlled slice before broad rollout.
Monitor for KPI movement and unintended regressions.
This is the correct enterprise posture: Smart Insights can accelerate improvement, but it should not bypass review when recommendations change what an agent is allowed to say or do.
Evaluation criteria
A buyer should leave this page knowing how to evaluate an AI improvement engine. The point is not whether the system can generate attractive recommendations. The point is whether those recommendations survive evidence, review, deployment, and measurement.
Evaluation question | Strong signal | Weak signal |
Does it start from the customer’s chosen KPI? | Recommendations are tied to resolution, escalation, containment, CSAT, revenue, retention, or another explicit objective. | The system surfaces generic “interesting patterns.” |
Does it show transcript evidence? | Every recommendation can be traced to example calls and affected volume. | The recommendation is plausible but not auditable. |
Can it distinguish root cause from symptom? | A transfer cluster is mapped to missing API access, failed fallback, policy gap, or knowledge issue. | It only says transfer rate is high. |
Can humans approve or reject updates? | The console supports review and controlled implementation. | The system treats every AI suggestion as automatically correct. |
Does it measure post-update impact? | Teams can see whether the selected KPI moved after deployment. | The update disappears into the backlog after approval. |
Can it handle ambiguous conversations? | Multi-intent and partial-resolution conversations are surfaced for review rather than overclassified. | Ambiguity is hidden behind simple labels. |
Does it create new agent context safely? | Policy changes, knowledge additions, and tool actions are tested before production. | Prompt edits are made directly without validation. |
Known limits
A pattern is not automatically a root cause. A cluster can identify repeated behavior, but the proposed explanation still needs evidence and human validation.
High-volume does not always mean high-value. Some frequent issues may be low business impact; some rare issues may be compliance-critical or revenue-critical.
Transcript quality affects insight quality. Noise, incomplete capture, redaction, missing fields, or poor integrations can reduce confidence.
Customer documentation may be incomplete. In the touchbase conversation, one product truth was that Giga sometimes has to help clients construct the resources needed to make the agent reliable. That same constraint affects insights.
Policy updates can create regressions. A change that improves one cluster may harm another if it is not tested against edge cases.
Projected KPI impact is not production proof. Projections should be treated as hypotheses until measured after deployment.
Human judgment remains necessary when the recommendation changes compliance-sensitive behavior, refund logic, eligibility decisions, or escalation boundaries.
Active research and improvement loop
Every repeated transfer, abandoned call, failed fallback, unresolved ticket, corrected hallucination, customer clarification, and post-call field can become evidence. The improvement loop is not only about making the next dashboard prettier. It is about making the agent’s next production version better than the last one.
Active research direction Use production conversation evidence to recommend instruction rewrites, knowledge-base repairs, API/tool additions, fallback workflows, escalation changes, and hallucination-sensitive guardrails, then measure whether those changes improve the KPI the customer chose. |
Bring one support KPI, one week of transcripts, and one recurring failure your team already suspects. Giga can show which conversations support the hypothesis, what the likely root cause is, what change the agent needs, and how to measure whether the update worked.
FAQ
What is Smart Insights for support agents?
Smart Insights is Giga’s agent-improvement layer. It analyzes production conversations, surfaces repeated patterns, ties them to selected KPIs, validates root-cause hypotheses against transcripts, and recommends changes that can improve support agent behavior.
How is Smart Insights different from a support analytics dashboard?
A dashboard reports performance. Smart Insights should explain why performance changed and what the agent needs next: a policy update, fallback workflow, knowledge repair, API/tool access, deterministic action, or conversational design change.
What kinds of KPIs can Smart Insights optimize for?
Common objectives include resolution rate, transfer rate, containment, escalation rate, customer satisfaction, revenue recovery, retention risk, compliance handling, and operational efficiency. The exact KPI should be selected before the system searches for improvement opportunities.
How does the system avoid chasing noisy patterns?
Useful insights should be evaluated by affected volume, relation to the selected KPI, transcript evidence, severity, business value, and human review. Not every visible cluster should become a production change.
Can Smart Insights recommend policy updates automatically?
It can recommend updates, but enterprise teams should review and approve changes before deployment, especially when the recommendation affects customer promises, eligibility, refunds, compliance, or escalation logic.
What is an example of a Smart Insights recommendation?
If hundreds of conversations transfer because customers ask for delivery confirmation and the agent lacks access to confirmation status, Smart Insights may recommend adding a fallback flow, exposing an API, or creating a deterministic lookup action.
How does Smart Insights use transcripts?
Transcripts provide the evidence layer. They help validate whether a pattern is real, what customers actually asked, how the agent responded, whether the issue was resolved, and what system gap caused the failure.
Where should humans stay in the loop?
Humans should approve recommendations, evaluate ambiguous root causes, validate sensitive policy changes, review projected impact, and decide when to deploy controlled updates.
